Analysis of ALL Pimp BIG experiment
file preparation
PimpBig2 experiment analysis - loading the data
setwd("/Users/magdalena/Dropbox/DataAndAnalysis/Tomato/PimpiBIG csv raw files/")
list.files() [1] "20201112_PimpiBig2_analysis.Rmd"
[2] "20210203_PimpiBig_analysis.nb.html"
[3] "20210203_PimpiBig_analysis.Rmd"
[4] "all_trait_clusters_control.txt"
[5] "all_trait_clusters.txt"
[6] "Fig.Growth_rates_histograms_MR_noLR_aLRL.pdf"
[7] "Fig.Lateral_root_distribution_across_MRL_DAS1_DAS4_Big01_to_Big04.pdf"
[8] "Fig.STI_based_clusters_STI_trait_comparison.pdf"
[9] "Fig.STI_clustering.pdf"
[10] "Fig.TRS_MRL_LRL_LRno_CoG_MRLpTRS_Big01_to_Big04.pdf"
[11] "for_Arjun"
[12] "pimpi_Big1_D0_D1.csv"
[13] "pimpi_Big1_D1_D2.csv"
[14] "pimpi_Big1_D2_D3.csv"
[15] "pimpi_Big1_D4_D5.csv"
[16] "pimpi_BIG2_arbor_stats.csv"
[17] "pimpi_Big2_D0_D1.csv"
[18] "pimpi_Big2_D2_D3.csv"
[19] "pimpi_Big2_D4_D5.csv"
[20] "pimpi_Big2_manual-scoring-last-day.csv"
[21] "pimpi_Big3_D0_D1.csv"
[22] "pimpi_Big3_D2_D3.csv"
[23] "pimpi_Big3_D3_D4.csv"
[24] "pimpi_Big3_D4_D5.csv"
[25] "pimpi_Big4_D0_D1.csv"
[26] "pimpi_Big4_D0_FU.csv"
[27] "pimpi_Big4_D0.csv"
[28] "pimpi_Big4_D2_D3.csv"
[29] "pimpi_Big4_D3_D4.csv"
[30] "pimpi_Big4_D4_D5.csv"
[31] "STI_clusters_better.txt"
[32] "STI_clusters.txt" Big1_D0D1 <- read.csv("pimpi_Big1_D0_D1.csv")
Big1_D2D3 <- read.csv("pimpi_Big1_D2_D3.csv")
Big1_D4D5 <- read.csv("pimpi_Big1_D4_D5.csv")
Big2_D0D1 <- read.csv("pimpi_Big2_D0_D1.csv")
Big2_D2D3 <- read.csv("pimpi_Big2_D2_D3.csv")
Big2_D4D5 <- read.csv("pimpi_Big2_D4_D5.csv")
Big3_D0D1 <- read.csv("pimpi_Big3_D0_D1.csv")
Big3_D2D3 <- read.csv("pimpi_Big3_D2_D3.csv")
Big3_D4D5 <- read.csv("pimpi_Big3_D4_D5.csv")
Big4_D0D1 <- read.csv("pimpi_Big4_D0_D1.csv")
Big4_D0_FU <- read.csv("pimpi_Big4_D0_FU.csv")
Big4_D2D3 <- read.csv("pimpi_Big4_D2_D3.csv")
Big4_D4D5 <- read.csv("pimpi_Big4_D4_D5.csv")
head(Big4_D0D1)head(Big4_D0_FU)# Need to add this because otherwise we have images that are called exactly the same and it is causing problems later on
Big4_D0_FU$image <- gsub(".rsml", "_FU.rsml", Big4_D0_FU$image)
head(Big4_D0_FU)head(Big4_D2D3)head(Big4_D4D5)Let’s fuse all the files into one big file per experiment
Big1 <- rbind(Big1_D0D1, Big1_D2D3)
Big1 <- rbind(Big1, Big1_D4D5)
Big2 <- rbind(Big2_D0D1, Big2_D2D3)
Big2 <- rbind(Big2, Big2_D4D5)
Big3 <- rbind(Big3_D0D1, Big3_D2D3)
Big3 <- rbind(Big3, Big3_D4D5)
Big4 <- rbind(Big4_D0D1, Big4_D0_FU)
Big4 <- rbind(Big4, Big4_D2D3)
Big4 <- rbind(Big4, Big4_D4D5)Let’s make sure we have a collumn indicating the experimental batch as well:
Big1$experiment <- "Big01"
Big2$experiment <- "Big02"
Big3$experiment <- "Big03"
Big4$experiment <- "Big04"There is an issue with the images from Big02 also being in Big04 - so we have to make sure that we have only images from particular experiment in there. So - we need to make a collumn for everyone with “date” to make sure we only have the following days
Big 1 experiment:
Big1$info <- strsplit(Big1$image, "_")
dim(Big1)[1] 8555 21Big1$date <- "none"
for(i in 1:8555){
Big1$date[i] <- sapply(Big1$info[i], function(x){
x[4]})
}
unique(Big1$date)[1] "20190909" "20190910" "20190911" "20190912" "20190913"Big 2 experiment:
Big2$info <- strsplit(Big2$image, "_")
dim(Big2)[1] 13695 21Big2$date <- "none"
for(i in 1:13695){
Big2$date[i] <- sapply(Big2$info[i], function(x){
x[4]})
}
unique(Big2$date)[1] "20190923" "20190924" "20190926" "20190925" "20190927"Big 3 experiment:
Big3$info <- strsplit(Big3$image, "_")
dim(Big3)[1] 13232 21Big3$date <- "none"
for(i in 1:13232){
Big3$date[i] <- sapply(Big3$info[i], function(x){
x[4]})
}
unique(Big3$date)[1] "20190930" "20191001" "20191002" "20191003" "20191004"Big 4 experiment:
Big4$info <- strsplit(Big4$image, "_")
dim(Big4)[1] 13346 21Big4$date <- "none"
for(i in 1:13346){
Big4$date[i] <- sapply(Big4$info[i], function(x){
x[4]})
}
unique(Big4$date)[1] "20191009" "20191008" "20191010" "20191012" "20191011"Now we can fuse all the data into one:
all_data <- rbind(Big1, Big2)
all_data <- rbind(all_data, Big3)
all_data <- rbind(all_data, Big4)
dim(all_data)[1] 48828 22Then - we need to check how many “Root” values we have per unique image and how many “Lateral root” values we have. This will save us heaps of headache in the future
In total we have X many images
length(unique(all_data$image))[1] 5521and we have X many Main Roots - which should be the same as image number…
onlyMr <- subset(all_data, all_data$root_order == 0)
dim(onlyMr)[1] 6661 22… but it isn’t - so let’s figure which images have more MR per image than 1 and remove these specific values (or decide which one to remove :)
We need to make a file where: Col. A > image Col. B > # Root Col. C > # Lateral Root
names <- c(text="image", "MR.no", "LR.no")
check_table <- data.frame()
for (k in names) check_table[[k]] <- as.character()Then - we will subset the entire dataset for one image and calculate how many MR and LR are there and integrate it into the table
i=1
uni <- subset(all_data, all_data$image == unique(all_data$image)[i])
uniMR <- subset(uni, uni$root_order == 0)
LR <- subset(uni, uni$root_order == 1)
check_table[i,1] <- as.character(unique(uni$image))
check_table[i,2] <- dim(MR)[1]
check_table[i,3] <- dim(LR)[1]
check_tableCool - now let’s loop it for all the images:
for (i in 2:5521) {
uni <- subset(all_data, all_data$image == unique(all_data$image)[i])
uni
MR <- subset(uni, uni$root_order == 0)
LR <- subset(uni, uni$root_order == 1)
check_table[i,1] <- as.character(unique(uni$image))
check_table[i,2] <- dim(MR)[1]
check_table[i,3] <- dim(LR)[1]
}
check_tableCool - now let’s fish out all the images where we have multiple MR:
suspicious <- subset(check_table, check_table$MR.no > 1)
dim(suspicious)[1] 1130 3suspiciousLet’s have a closer look at these suspicious images:
closer_look <- subset(all_data, all_data$image == suspicious$image[100])
closer_look2 <- subset(closer_look, closer_look$root_order == 0)
closer_look2It looks like these double roots are just duplications - let’s see what happens when we remove exact duplicates using tidyverse package
library(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
[30m── [1mAttaching packages[22m ───────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──[39m
[30m[32m✓[30m [34mggplot2[30m 3.3.1 [32m✓[30m [34mpurrr [30m 0.3.4
[32m✓[30m [34mtibble [30m 3.0.1 [32m✓[30m [34mdplyr [30m 1.0.0
[32m✓[30m [34mtidyr [30m 1.1.0 [32m✓[30m [34mstringr[30m 1.4.0
[32m✓[30m [34mreadr [30m 1.3.1 [32m✓[30m [34mforcats[30m 0.5.0[39m
[30m── [1mConflicts[22m ──────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
[31mx[30m [34mdplyr[30m::[32mfilter()[30m masks [34mstats[30m::filter()
[31mx[30m [34mdplyr[30m::[32mlag()[30m masks [34mstats[30m::lag()[39mall_data2 <- all_data %>% distinct()
length(unique((all_data2$image)))[1] 5521onlyMr2 <- subset(all_data2, all_data2$root_order == 0)
dim(onlyMr2)[1] 5543 22cool - so now we have majority of the images where the MR is simply duplicated - so let’s repeat the exercise from above again to identify what other images we have left over which have two MR:
names <- c(text="image", "MR.no", "LR.no")
check_table2 <- data.frame()
for (k in names) check_table2[[k]] <- as.character()
for (i in 1:5521) {
uni <- subset(all_data2, all_data2$image == unique(all_data2$image)[i])
MR <- subset(uni, uni$root_order == 0)
LR <- subset(uni, uni$root_order == 1)
check_table2[i,1] <- as.character(unique(uni$image))
check_table2[i,2] <- dim(MR)[1]
check_table2[i,3] <- dim(LR)[1]
}
suspicious <- subset(check_table2, check_table2$MR.no > 1)
suspiciousLet’s have a closer look at these images and their MRs:
closer_look <- subset(all_data2, all_data2$image == suspicious$image[1])
closer_look1 <- subset(closer_look, closer_look$root_order == 0)
closer_look1
closer_look <- subset(all_data2, all_data2$image == suspicious$image[22])
closer_look2 <- subset(closer_look, closer_look$root_order == 0)
closer_look2OK - so clearly - we have something that wasnt classified as LR properly (as in case of closer_look1).
We can recognize the “mistake MR” by saying that the name would start with root_ but let’s see if this is the case for many of them:
onlyMr <- subset(all_data2, all_data2$root_order == 0)
unique(onlyMr$root_name) [1] " 288_2_C" " 189_3_C" " 214_2_S" " 201_1_C" " root_0" " 292_3_S" " 306_1_C" " 288_1_C" " 213_3_S"
[10] " 207_3_S" " 212_1_S" " 207_3_C" " 199_1_S" " 307_2_C" " 282_3_C" " 199_2_S" " 298_3_S" " 212_3_C"
[19] " 212_2_C" " 278_1_S" " 301_1_C" " 201_1_S" " 286_1_S" " 287_1_S" " 213_2_C" " 220_2_C" " 286_2_S"
[28] " 214_1_C" " 286_2_C" " 194_1_S" " 212_1_C" " 294_2_C" " 213_1_C" " 287_2_S" " 285_2_S" " 293_2_C"
[37] " 284_2_S" " 195_3_C" " 277_1_C" " 285_3_S" " 278_1_C" " 300_1_S" " 214_3_C" " 196_2_S" " 292_1_C"
[46] " 283_2_C" " 201_2_S" " 307_1_S" " 279_1_S" " 298_1_S" " 293_3_S" " 280_1_S" " 194_3_S" " 294_3_S"
[55] " 299_2_C" " 291_2_S" " 207_1_S" " 298_3_C" " 282_1_C" " 285_3_C" " 280_3_C" " 307_3_C" " 200_1_S"
[64] " 211_3_S" " 308_3_C" " 284_1_S" " 298_1_C" " 308_2_C" " 197_3_S" " 290_2_C" " 287_1_C" " 301_1_S"
[73] " 196_1_C" " 284_2_C" " 299_1_C" " 220_3_C" " 278_3_C" " 293_2_S" " 308_3_S" " 207_1_C" " 283_2_S"
[82] " 306_2_S" " 280_1_C" " 220_1_C" " 200_3_S" " 214_3_S" " 213_2_S" " 287_3_S" " 195_3_S" " 290_1_S"
[91] " 194_3_C" " 194_1_C" " 288_3_S" " 195_2_C" " 282_2_S" " 290_3_C" " 196_2_C" " 200_2_S" " 300_2_C"
[100] " 198_2_C" " 211_1_C" " 200_1_C" " 285_2_C" " 197_1_C" " 288_2_S" " 292_2_C" " 308_1_S" " 301_2_C"
[109] " 279_3_C" " 299_3_S" " 195_2_S" " 294_2_S" " 279_2_C" " 289_2_S" " 282_2_C" " 301_3_S" " 299_1_S"
[118] " 300_3_C" " 197_2_C" " 286_1_C" " 298_2_C" " 288_3_C" " 194_2_S" " 306_2_C" " 213_3_C" " 307_1_C"
[127] " 201_3_C" " 291_1_S" " 291_3_C" " 277_2_S" " 220_1_S" " 2800_3_S" " 280_2_C" " 196_1_S" " 207_2_S"
[136] " 235_1_C" " 279_1_C" " 278_3_S" " 199_2_C" " 287_3_C" " 195_1_S" " 290_2_S" " 306_3_S" " 213_1_S"
[145] " 294_1_C" " 294_1_S" " 290_3_S" " 306_3_C" " 195_1_C" " 199_3_C" " 283_3_C" " 278_2_S" " 290_1_C"
[154] " 299_2_S" " 197_3_C" " 212_2_S" " 211_2_S" " 211_2_C" " 293_3_C" " 199_3_S" " 199_1_C" " 214_1_S"
[163] " 277_1_S" " 197_1_S" " 299_3_C" " 292_3_C" " 291_2_C" " 198_1_S" " 207_2_C" " 220_2_S" " 282_1_S"
[172] " 214_2_C" " 285_1_S" " 292_2_S" " 194_2_C" " 278_2_C" " 291_1_C" " 198_2_S" " 286_3_S" " 212_3_S"
[181] " 200_2_C" " 220_3_S" " 198_1_C" " 200_3_C" " 300_3_S" " 284_1_C" " 293_1_S" " 283_1_C" " 211_3_C"
[190] " 283_3_S" " 201_2_C" " 286_3_C" " 306_1_S" " 277_2_C" " 293_4_C" " 279_3_S" " 283_1_S" " 307_3_S"
[199] " 307_2_S" " 211_1_S" " 197_2_S" " 308_2_S" " 301_2_S" " 288_1_S" " 300_2_S" " 282_3_S" " 287_2_C"
[208] " 301_3" " 291_3_S" " 294_3_C" " 198_3_S" " root_1" " 308_1_C" " 201_3_S" " 192_3_C" " 171_3_C"
[217] " 147_3_C" " 252_1_S" " 252_3_S" " 258_1_S" " 166_1_C" " 269_3_C" " 064_1_C" " 253_2_S" " 264_2_S"
[226] " 193_1_S" " 146_3_C" " 141_2_C" " 267_1_C" " 256_3_S" " 061_1_S" " 062_2_S" " 065_2_S" " 251_3_S"
[235] " 064_1_S" " 139_3_S" " 141_1_S" " 169_1_S" " 083_3_S" " 258_3_S" " 138_1_C" " 083_2_S" " 192_1_C"
[244] " 172_2_S" " 144_2_C" " 168_1_S" " 268_1_S" " 147_2_C" " 253_3_S" " 193_1_C" " 264_2_C" " 165_2_S"
[253] " 138_3_S" " 146_2_C" " 258_2_S" " 061_2_C" " 065_3_S" " 262_3_S" " 140_3_C" " 260_2_S" " 170_3_C"
[262] " 141_1_C" " 144_1_C" " 142_4_S" " 258_1_C" " 259_1_C" " 270_1_C" " 139_1_S" " 191_1_S" " 168_3_S"
[271] " 272_2_C" " 138_4_C" " 269_3_S" " 147_1_S" " 165_1_S" " 144_2_S" " 255_1_S" " 147_3_S" " 138_4_S"
[280] " 170_1_S" " 060_1_S" " 272_3_S" " 065_3_C" " 260_2_C" " 251_2_C" " 269_1_C" " 172_2_C" " 262_3_C"
[289] " 261_1_S" " 261_2_S" " 192_3_S" " 166_3_C" " 168_3_C" " 260_1_C" " 167_1_S" " 262_2_C" " 066_2_S"
[298] " 253_2_C" " 142_3_C" " 060_3_S" " 165_2_C" " 066_2_C" " 136_3_C" " 251_1_S" " 191_2_C" " 142_3_S"
[307] " 140_2_S" " 141_2_S" " 143_1_S" " 256_2_S" " 139_2_C" " 193_3_S" " 143_2_C" " 064_2_S" " 262_1_C"
[316] " 251_3_C" " 138_2_C" " 165_1_C" " 252_3_C" " 139_2_S" " 083_3_C" " 166_1_S" " 171_3_S" " 262_4_C"
[325] " 167_1_C" " 256_1_C" " 264_1_C" " 146_1_C" " 259_3_S" " 143_2_S" " 136_2_C" " 170_2_C" " 146_2_S"
[334] " 192_2_C" " 061_3_C" " 255_2_C" " 147_1_C" " 259_2_C" " 140_2_C" " 169_2_C" " 258_3_C" " 146_3_S"
[343] " 169_3_S" " 138_2_S" " 061_1_C" " 267_1_S" " 170_3_S" " 252_2_S" " 141_3_C" " 140_3_S" " 060_2_S"
[352] " 166_3_S" " 082_3_C" " 136_1_C" " 141_3_S" " 168_2_C" " 165_3_C" " 270_2_S" " 272_3_C" " 193_3_C"
[361] " 256_2_C" " 252_2_C" " 140_1_C" " 142_2_C" " 170_2_S" " 171_2_C" " 144_3_C" " 193_2_S" " 262_2_S"
[370] " 144_1_S" " 272_1_C" " 259_3_C" " 138_1_S" " 191_3_S" " 062_1_C" " 083_1_S" " 147_2_S" " 172_1_C"
[379] " 252_1_C" " 136_2_S" " 170_1_C" " 063_1_S" " 260_3_C" " 264_3_C" " 142_4_C" " 270_2_C" " 256_3_C"
[388] " 140_1_S" " 269_2_S" " 065_1_C" " 191_1_C" " 066_1_S" " 061_2_S" " 171_2_S" " 142_1_S" " 082_3_S"
[397] " 256_1_S" " 193_2_C" " 253_1_C" " 168_2_S" " 253_1_S" " 142_2_S" " 259_2_S" " 065_2_C" " 169_2_S"
[406] " 143_1_C" " 171_1_C" " 264_3_S" " 260_3_S" " 060_3_C" " 192_1_S" " 171_1_S" " 261_2_C" " 169_1_C"
[415] " 258_2_C" " 063_1_C" " 261_3_C" " 082_2_C" " 083_2_C" " 082_1_S" " 259_1_S" " 191_2_S" " 169_3_C"
[424] " 168_1_C" " 272_2_S" " 172_3_C" " 255_1_C" " 061_3_S" " 261_3_S" " 138_3_C" " 261_1_C" " 139_3_C"
[433] " 136_3_S" " 082_1_C" " 264_1_S" " 268_2_S" " 082_2_S" " 270_1_S" " 172_1_S" " 262_4_S" " 269_2_C"
[442] " 253_3_C" " 251_1_C" " 191_3_C" " 272_1_S" " 262_1_S" " 268_2_C" " 192_2_S" " 066_1_C" " 144_3_S"
[451] " 251_2_S" " 136_1_S" " 142_1_C" " 255_2_S" " 166_2_S" " 083_1_C" " 267_2_C" " 060_1_C" " 268_3_C"
[460] " 146_1_S" " 065_1_S" " 165_3_S" " 268_3_S" " 269_1_S" " 166_2_C" " 260_1_S" " 172_3_S" " 060_2_C"
[469] " 139_1_C" " root_5" " 230_2_C" " 174_1_S" " 234_1_C" " 245_3_S" " 173_4_C" " 236_3_S" " 084_3_C"
[478] " 240_4_C" " 229_4_C" " 245_1_S" " 236_4_C" " 241_1_C" " 231_2_S" " 175_1_S" " 176_3_S" " 245_4_S"
[487] " 064_3_S" " 228_1_C" " 244_2_S" " 244_1_S" " 227_1_C" " 237_1_C" " 228_1_S" " 174_3_C" " 176_1_C"
[496] " 229_4_S" " 274_1_C" " 249_3_S" " 090_3_S" " 090_1_S" " 247_2_S" " 235_2_S" " 091_4_C" " 087_1_S"
[505] " 091_4_S" " 090_3_C" " 085_2_S" " 227_2_C" " 237_3_C" " 228_2_C" " 230_3_s" " 175_4_C" " 250_1_C"
[514] " 222_2_S" " 093_3_S" " 229_2_S" " 239_4_C" " 240_1_C" " 229_3_S" " 233_1_C" " 250_4_S" " 274_2_C"
[523] " 093_1_C" " 091_1_C" " 247_3_S" " 088_1_C" " 230_1_S" " 234_2_C" " 271_2_S" " 173_1_C" " 240_2_S"
[532] " 092_4_C" " 084_1_S" " 227_3_C" " 084_1_C" " 229_3_C" " 221_1_S" " 233_3_S" " 088_2_S" " 236_1_S"
[541] " 239_4_S" " 236_2_S" " 221_1_C" " 247_4_S" " 225_1_C" " 234_1_S" " 173_3_C" " 233_2_C" " 089_3_C"
[550] " 234_2_S" " 225_1_S" " 241_2_C" " 084_3_S" " 247_1_S" " 245_3_C" " 089_1_" " 247_3_C" " 239_3_C"
[559] " 244_3_S" " 038_2_S" " 250_3_S" " 085_1_S" " 176_1_S" " 228_3_C" " 234_3_S" " 038_3_S" " 273_1_S"
[568] " 248_1_C" " 247_2_C" " 298_2_S" " 228_4_C" " 248_4_C" " 089_2_S" " 274_3_C" " 086_3_C" " 231_2_C"
[577] " 239_1_C" " 173_1_S" " 085_3_S" " 086_1_C" " 085_2_C" " 093_2_C" " 228_2_S" " 084_2_S" " 231_3_C"
[586] " 273_3_C" " 175_3_C" " 176_2_S" " 091_2_S" " 271_1_C" " 084_4_C" " 273_2_C" " 173_4_S" " 240_4_S"
[595] " 228_3_S" " 274_4_C" " 239_1_S" " 236_2_C" " 084_2_C" " 173_2_C" " 230_2_S" " 093_2_S" " 093_4_C"
[604] " 092_1_C" " 250_4_C" " 240_3_C" " 174_1_C" " 038_1_S" " 176_4_C" " 092_3_C" " 244_3_C" " 176_2_C"
[613] " 240_2_C" " 241_2_S" " 274_1_S" " 064_2_C" " 088_1_S" " 227_2_S" " 240_1_S" " 087_2_S" " 221_2_C"
[622] " 088_3_C" " 092_2_C" " 174_3_S" " 235_1_S" " 090_2_S" " 086_3_S" " 086_1_S" " 089_2_C" " 249_2_S"
[631] " 176_4_S" " 231_1_C" " 092_3_S" " 091_3_S" " 093_3_C" " 229_1_S" " 175_2_S" " 236_3_C" " 087_3_C"
[640] " 092_2_S" " 239_2_S" " 248_2_C" " 229_1_C" " 250_3_C" " 222_2_C" " 225_2_C" " 038_3_C" " 222_1_S"
[649] " 274_4_S" " 233_3_C" " 230_1_C" " 090_1_C" " 236_4_S" " 175_2_C" " 248_1_S" " 271_4_S" " 064_3_C"
[658] " 175_1_C" " 093_1_S" " 249_2_C" " 173_2_S" " 087_4_C" " 230_3_C" " 225_2_S" " 090_2_C" " 273_1_C"
[667] " 247_1_C" " 237_2_C" " 084_4_S" " 249_3_C" " 239_2_C" " 237_4_C" " 237_3_S" " 231_1_S" " 271_1_S"
[676] " 239_3_S" " 233_4_S" " 271_2_C" " 175_3_S" " 087_4_S" " 092_1_S" " 233_1_S" " 174_4_C" " 038_1_C"
[685] " 174_4_S" " 087_1_C" " 228_4_S" " 092_4_S" " 231_3_S" " 273_2_S" " 176_3_C" " 247_4_C" " 248_4_S"
[694] " 245_2_S" " 240_3_S" " 241_3_C" " 245_1_C" " 091_1_S" " 233_2_S" " 241_1_S" " 085_1_C" " 174_2_S"
[703] " 089_4_C" " 244_1_C" " 087_2_C" " 248_3_C" " 227_3_S" " 273_3_S" " 250_2_C" " 271_3_C" " 273_4_C"
[712] " 222_1_C" " 248_2_S" " 249_1_S" " 093_4_S" " 271_3_S" " 236_1_C" " 089_3_S" " 237_1_S" " 248_3_S"
[721] " 229_2_C" " 273_4_S" " 091_2_C" " 085_3_C" " 175_4_S" " 244_2_C" " 088_2_C" " 089_4_S" " 271_4_C"
[730] " 086_2_S" " 237_4_S" " 227_1_S" " 087_3_S" " 089_1_S" " 250_2_S" " 274_2_S" " 091_3_C" " 038_2_C"
[739] " 174_2_C" " 274_3_S" " 234_3_C" " 241_3_S" " 233_4_C" " 245_2_C" " 237_2_S" " 173_3_S" " 086_2_C"
[748] " 249_1_C" " 088_3_S" " 230_3_S" " 089_1_C" " 250_1_S" " 237_1_s" " 031_3_S" " 030_3_C" " 103_4_S"
[757] " 028_3_S" " 098_1_S" " 055_2_S" " 075_3_C" " 059_4_C" " 095_4_C" " 101_1_S" " 034_1_C" " 002_1_C"
[766] " 030_2_S" " 103_3_S" " 056_3_S" " 001_3_C" " 099_3_C" " 102_4_C" " 012_3_C" " 076_1_C" " 098_1_C"
[775] " 079_1_C" " 080_2_S" " 104_3_S" " 005_3_S" " 080_1_S" " 006_2_C" " 102_3_C" " 081_1_C" " 101_2_C"
[784] " 096_1_C" " 101_2_S" " 058_4_C" " 028_4_C" " 059_3_S" " 099_2_S" " 104_3_C" " 103_3_C" " 079_4_C"
[793] " 030_1_S" " 105_4_S" " 040_1_C" " 080_4_S" " 029_3_C" " 036_2_C" " 058_1_C" " 057_3_S" " 035_4_C"
[802] " 040_1_S" " 010_2_S" " 094_3_C" " 032_3_S" " 011_1_C" " 010_2_C" " 074_4_C" " 032_4_S" " 056_2_C"
[811] " 103_1_S" " 103_1_C" " 022_4_C" " 010_1_S" " 102_2_S" " 105_3_C" " 081_2_S" " 003_3_S" " 056_2_S"
[820] " 095_1_S" " 009_3_S" " 011_4_S" " 002_2_S" " 106_1_S" " 098_3_C" " 058_2_S" " 040_3_S" " 103_2_S"
[829] " 075_2_C" " 001_4_S" " 009_2_S" " 081_1_S" " 057_1_C" " 028_3_C" " 033_2_C" " 012_1_S" " 006_3_C"
[838] " 080_1_C" " 098_3_S" " 094_2_C" " 029_3_S" " 058_4_S" " 074_3_C" " 095_4_S" " 028_1_S" " 013_3_C"
[847] " 097_1_S" " 001_2_S" " 031_1_S" " 105_1_S" " 040_2_C" " 094_2_S" " 007_2_C" " 075_3_S" " 013_2_S"
[856] " 074_4_S" " 007_1_C" " 006_3_S" " 035_2_C" " 008_1_S" " 057_1_S" " 007_3_C" " 059_3_C" " 076_3_C"
[865] " 012_3_S" " 008_3_C" " 007_1_S" " 094_3_S" " 099_4_C" " 057_3_C" " 099_1_C" " 040_4_C" " 035_2_S"
[874] " 029_4_S" " 104_2_S" " 030_2_C" " 012_2_S" " 103_4_C" " 079_3_S" " 105_3_S" " 007_2_S" " 032_2_C"
[883] " 012_4_S" " 057_4_S" " 013_1_C" " 032_1_C" " 055_2_C" " 010_3_S" " 009_2_C" " 078_3_S" " 099_1_S"
[892] " 001_1_C" " 057_2_S" " 098_4_S" " 079_2_S" " 056_3_C" " 003_3_C" " 031_2_C" " 106_2_C" " 001_2_C"
[901] " 005_3_C" " 059_1_C" " 096_2_S" " 009_1_C" " root_3" " 081_3_C" " 081_4_S" " 059_2_C" " 037_1_C"
[910] " 031_4_C" " 038_4_C" " 057_2_C" " 057_4_C" " 030_1_C" " 078_1_C" " 035_1_S" " 029_1_C" " 101_3_S"
[919] " 055_1_S" " 008_1_C" " 034_1_S" " 005_2_S" " 011_3_C" " 095_2_S" " 001_1_S" " 055_3_C" " 003_2_S"
[928] " 105_4_C" " 104_4_S" " 031_4_S" " 102_4_S" " 059_2_S" " 013_2_C" " 079_3_C" " 009_3_C" " 074_2_S"
[937] " 006_1_C" " 010_1_C" " 075_1_S" " 008_3_S" " 009_4_C" " 104_1_S" " 058_3_C" " 008_4_C" " 011_4_C"
[946] " 010_3_C" " 078_1_S" " 058_2_C" " 029_1_S" " 076_2_C" " 099_4_S" " 106_2_S" " 075_1_C" " 009_1_S"
[955] " 008_4_S" " 095_3_C" " 080_2_C" " 099_2_C" " 033_2_S" " 074_1_S" " 003_1_S" " 002_1_S" " 011_3_S"
[964] " 105_2_C" " 059_4_S" " 080_3_S" " 055_3_S" " 028_1_C" " 008_2_S" " 035_3_C" " 011_2_C" " 094_1_C"
[973] " 013_3_S" " 034_2_C" " 055_1_C" " 056_1_S" " 005_2_C" " 005_4_C" " 035_3_S" " 028_2_S" " 005_4_S"
[982] " 058_3_S" " 104_2_C" " 056_1_C" " 101_4_S" " 079_4_S" " 012_2_C" " 002_4_S" " 101_4_C" " 031_1_C"
[991] " 059_1_S" " 105_1_C" " 095_3_S" " 097_1_C" " 098_4_C" " 008_2_C" " 033_3_S" " 029_4_C" " 094_1_S"
[1000] " 034_3_C"
[ reached getOption("max.print") -- omitted 83 entries ]ok - so then we have to split the root name into individual tiers and select MR which start with “root_”:
all_data2$root_info <- strsplit(all_data2$root_name, "_")
dim(all_data2)[1] 41317 23for(i in 1:41317){
all_data2$genotype[i] <- sapply(all_data2$root_info[i], function(x){
x[1]})
all_data2$rep[i] <- sapply(all_data2$root_info[i], function(x){
x[2]})
all_data2$cond[i] <- sapply(all_data2$root_info[i], function(x){
x[3]})
}
head(all_data2)Now we have to subset for Mr and look at their individual genotype information
onlyMr <- subset(all_data2, all_data2$root_order == 0)
dim(onlyMr)[1] 5543 26unique(onlyMr$genotype) [1] " 288" " 189" " 214" " 201" " root" " 292" " 306" " 213" " 207" " 212" " 199" " 307" " 282" " 298"
[15] " 278" " 301" " 286" " 287" " 220" " 194" " 294" " 285" " 293" " 284" " 195" " 277" " 300" " 196"
[29] " 283" " 279" " 280" " 299" " 291" " 200" " 211" " 308" " 197" " 290" " 198" " 289" " 2800" " 235"
[43] " 192" " 171" " 147" " 252" " 258" " 166" " 269" " 064" " 253" " 264" " 193" " 146" " 141" " 267"
[57] " 256" " 061" " 062" " 065" " 251" " 139" " 169" " 083" " 138" " 172" " 144" " 168" " 268" " 165"
[71] " 262" " 140" " 260" " 170" " 142" " 259" " 270" " 191" " 272" " 255" " 060" " 261" " 167" " 066"
[85] " 136" " 143" " 082" " 063" " 230" " 174" " 234" " 245" " 173" " 236" " 084" " 240" " 229" " 241"
[99] " 231" " 175" " 176" " 228" " 244" " 227" " 237" " 274" " 249" " 090" " 247" " 091" " 087" " 085"
[113] " 250" " 222" " 093" " 239" " 233" " 088" " 271" " 092" " 221" " 225" " 089" " 038" " 273" " 248"
[127] " 086" " 031" " 030" " 103" " 028" " 098" " 055" " 075" " 059" " 095" " 101" " 034" " 002" " 056"
[141] " 001" " 099" " 102" " 012" " 076" " 079" " 080" " 104" " 005" " 006" " 081" " 096" " 058" " 105"
[155] " 040" " 029" " 036" " 057" " 035" " 010" " 094" " 032" " 011" " 074" " 022" " 003" " 009" " 106"
[169] " 033" " 013" " 097" " 007" " 008" " 078" " 037" onlyMr$genotype <- gsub(" ", "", onlyMr$genotype)
unique(onlyMr$genotype) [1] "288" "189" "214" "201" "root" "292" "306" "213" "207" "212" "199" "307" "282" "298" "278" "301"
[17] "286" "287" "220" "194" "294" "285" "293" "284" "195" "277" "300" "196" "283" "279" "280" "299"
[33] "291" "200" "211" "308" "197" "290" "198" "289" "2800" "235" "192" "171" "147" "252" "258" "166"
[49] "269" "064" "253" "264" "193" "146" "141" "267" "256" "061" "062" "065" "251" "139" "169" "083"
[65] "138" "172" "144" "168" "268" "165" "262" "140" "260" "170" "142" "259" "270" "191" "272" "255"
[81] "060" "261" "167" "066" "136" "143" "082" "063" "230" "174" "234" "245" "173" "236" "084" "240"
[97] "229" "241" "231" "175" "176" "228" "244" "227" "237" "274" "249" "090" "247" "091" "087" "085"
[113] "250" "222" "093" "239" "233" "088" "271" "092" "221" "225" "089" "038" "273" "248" "086" "031"
[129] "030" "103" "028" "098" "055" "075" "059" "095" "101" "034" "002" "056" "001" "099" "102" "012"
[145] "076" "079" "080" "104" "005" "006" "081" "096" "058" "105" "040" "029" "036" "057" "035" "010"
[161] "094" "032" "011" "074" "022" "003" "009" "106" "033" "013" "097" "007" "008" "078" "037" onlyMr2 <- subset(onlyMr, onlyMr$genotype != "root")
dim(onlyMr2)[1] 5520 26unique(onlyMr2$genotype) [1] "288" "189" "214" "201" "292" "306" "213" "207" "212" "199" "307" "282" "298" "278" "301" "286"
[17] "287" "220" "194" "294" "285" "293" "284" "195" "277" "300" "196" "283" "279" "280" "299" "291"
[33] "200" "211" "308" "197" "290" "198" "289" "2800" "235" "192" "171" "147" "252" "258" "166" "269"
[49] "064" "253" "264" "193" "146" "141" "267" "256" "061" "062" "065" "251" "139" "169" "083" "138"
[65] "172" "144" "168" "268" "165" "262" "140" "260" "170" "142" "259" "270" "191" "272" "255" "060"
[81] "261" "167" "066" "136" "143" "082" "063" "230" "174" "234" "245" "173" "236" "084" "240" "229"
[97] "241" "231" "175" "176" "228" "244" "227" "237" "274" "249" "090" "247" "091" "087" "085" "250"
[113] "222" "093" "239" "233" "088" "271" "092" "221" "225" "089" "038" "273" "248" "086" "031" "030"
[129] "103" "028" "098" "055" "075" "059" "095" "101" "034" "002" "056" "001" "099" "102" "012" "076"
[145] "079" "080" "104" "005" "006" "081" "096" "058" "105" "040" "029" "036" "057" "035" "010" "094"
[161] "032" "011" "074" "022" "003" "009" "106" "033" "013" "097" "007" "008" "078" "037" Now - lets get rid of these bad MR from the original all_data2 file:
badMr <- subset(onlyMr, onlyMr$genotype == "root")
badMrwrong_MR <- badMr$root
wrong_MR [1] " 506f645e-11ef-4f56-809d-b68535967791" " b6640da9-642f-4afe-8885-8aef081c64f2"
[3] " 4db776d9-a41e-41db-95d3-5bbaf22712fa" " 4db776d9-a41e-41db-95d3-5bbaf22712fa"
[5] " b671303e-fa10-4a09-b4ec-40d709f73703" " 1f9d1645-51c0-4c5d-9ef2-e15aacd51a65"
[7] " 4db776d9-a41e-41db-95d3-5bbaf22712fa" " db20a3aa-2bc0-422d-bd9b-0c03f785f936"
[9] " 4db776d9-a41e-41db-95d3-5bbaf22712fa" " 6c352368-f3d3-44a9-9d9b-1a6ac64b3636"
[11] " f2fa1001-0b9b-4714-ab14-9baec299245b" " e9280635-2275-4439-af3b-8d83ba551df1"
[13] " b6f9a393-91fa-40b8-b537-06a06ead5808" " a1c527ec-b83e-427b-8d3f-701df49f942f"
[15] " e93664ce-1b47-4d7b-821a-78b92049879c" " dd977aaa-fc38-4c4e-af58-fa3412af7478"
[17] " 049886f7-3a40-40b9-a4a9-160e7c2948d3" " 647a5d35-7a55-4281-8602-be4046f26ca1"
[19] " a3fa1354-196b-4b71-99fd-21545cb9e6aa" " 09d4bbd2-5f7f-4efe-a06e-aea8a173b02f"
[21] " cfa05ed0-4cca-4b09-8611-b315d8a8dc16" " 6b193102-e41c-401d-90ca-3278d1220e1e"
[23] " eecb776e-7fc2-4483-8a76-83b299756f67"all_data2_single_MR <- all_data2[!(all_data2$root %in% wrong_MR),]
dim(all_data2_single_MR)[1] 41276 26dim(all_data2)[1] 41317 26length(unique((all_data2_single_MR$image)))[1] 5520onlyMr3 <- subset(all_data2_single_MR, all_data2_single_MR$root_order == 0)
dim(onlyMr3)[1] 5513 26So we still have 7 images that supposedly contain more than 2 roots
Let’s look at these images:
names <- c(text="image", "MR.no", "LR.no")
check_table2 <- data.frame()
for (k in names) check_table2[[k]] <- as.character()
for (i in 1:5520) {
uni <- subset(all_data2_single_MR, all_data2_single_MR$image == unique(all_data2_single_MR$image)[i])
MR <- subset(uni, uni$root_order == 0)
LR <- subset(uni, uni$root_order == 1)
check_table2[i,1] <- as.character(unique(uni$image))
check_table2[i,2] <- dim(MR)[1]
check_table2[i,3] <- dim(LR)[1]
}
suspicious <- subset(check_table2, check_table2$MR.no > 1)
suspiciouscloser_look <- subset(all_data2_single_MR, all_data2_single_MR$image == suspicious$image[1])
closer_look1 <- subset(closer_look, closer_look$root_order == 0)
closer_look1
closer_look <- subset(all_data2_single_MR, all_data2_single_MR$image == suspicious$image[2])
closer_look2 <- subset(closer_look, closer_look$root_order == 0)
closer_look2from the look of it - it seems we have two cases where we have exported twice the exact same root but one longer than the other (I presume longer is correct one). Problem is that their root and image are both identical.
After inspecting the images - it seems that: _set1_day3_20191002_238.tiff has > 2 lateral roots _set1_day3_20191002_001 has > 3 lateral roots
so in both cases - the 2nd Main Root is better. I am affraid that we will also get some oddness with positions of lateral roots. So maybe let’s inspect all the data belonging to these two images:
odd_stuff01 <- subset(all_data2_single_MR, all_data2_single_MR$image == "_set1_day3_20191002_238.rsml")
odd_stuff02 <- subset(all_data2_single_MR, all_data2_single_MR$image == "_set1_day3_20191002_001.rsml")
odd_stuff01odd_stuff02ok - so based on this data - it seems like: - the first 3 rows in odd_stuff01 are containing faulty data, and thus should be removed - the first 4 rows in odd_stuff02 are containing faulty data, and thus should be removed
So let’s remove these columns from individual odd_stuff files, remove the entire picture from the all_data2_single_MR, and then rbind() the odd_stuff together back into the all_data2_single_MR:
odd_stuff01 <- odd_stuff01[4:13,]
odd_stuff01
odd_stuff02 <- odd_stuff02[5:14,]
odd_stuff02
bye <- c("_set1_day3_20191002_238.rsml", "_set1_day3_20191002_001.rsml")
all_data3 <- subset(all_data2_single_MR, !(all_data2_single_MR$image %in% bye))
all_data3 <- rbind(all_data3, odd_stuff01)
all_data3 <- rbind(all_data3, odd_stuff02)Let’s do a final check if we have NO other images that have two MR now:
length(unique(all_data3$image))[1] 5520names <- c(text="image", "MR.no", "LR.no")
check_table2 <- data.frame()
for (k in names) check_table2[[k]] <- as.character()
for (i in 1:5520) {
uni <- subset(all_data3, all_data3$image == unique(all_data3$image)[i])
MR <- subset(uni, uni$root_order == 0)
LR <- subset(uni, uni$root_order == 1)
check_table2[i,1] <- as.character(unique(uni$image))
check_table2[i,2] <- dim(MR)[1]
check_table2[i,3] <- dim(LR)[1]
}
suspicious <- subset(check_table2, check_table2$MR.no > 1)
suspiciousGreat - no suspicious images anymore - so we can move on :)
calculating desired traits:
Let’s now get rid of all the traits like volume and diameter that we are not directly interested in:
head(all_data3)colnames(all_data3) [1] "image" "root_name" "root" "length"
[5] "surface" "volume" "diameter" "root_order"
[9] "root_ontology" "parent_name" "parent" "insertion_position"
[13] "insertion_angle" "n_child" "child_density" "first_child"
[17] "insertion_first_child" "last_child" "insertion_last_child" "experiment"
[21] "info" "date" "root_info" "genotype"
[25] "rep" "cond" final <- all_data3[,c(1:3, 20, 22, 24:26, 4, 9, 11, 12,14,17,19)]
head(final)then - we need to create a file that has all MR:
only_MR <- subset(final, final$root_ontology == unique(final$root_ontology)[1])
colnames(only_MR)[9] <- "MRL"
head(only_MR)and since we dont need to calculate any LR related phenotypes for the plants that dont have LR - we need to subset our MR file a bit further:
no_LR_MR <- subset(only_MR, only_MR$n_child < 1)
only_MR <- subset(only_MR, only_MR$n_child > 0)
head(only_MR)head(no_LR_MR)Cool - there are now quite a lot of unneccessary information now - so let’s get to bare necessities:
colnames(only_MR) [1] "image" "root_name" "root" "experiment"
[5] "date" "genotype" "rep" "cond"
[9] "MRL" "root_ontology" "parent" "insertion_position"
[13] "n_child" "insertion_first_child" "insertion_last_child" only_MR <- only_MR[,c(1:9,13:15)]
no_LR_MR <- no_LR_MR[,c(1:9,13:15)]
dim(only_MR)[1] 4460 12head(only_MR)We have the same root ID occurring multiple times - as we are tracing individual plants accross the time and transferring the data from one image to another - therefore - let’s check if it all adds up:
all_MR <- unique(only_MR$root)
length(all_MR)[1] 1289all_img <- unique(only_MR$image)
length(all_img)[1] 4460length(all_img) / length(all_MR)[1] 3.460047only_MR$image_id <- paste(only_MR$image, "_", only_MR$root_name, sep="")
head(only_MR)
all_img_id <- unique(only_MR$image_id)
length(all_img_id)[1] 4460Almost - but we will deal with it later.
So we have in total 4460 unique images - we will have to examine the LR belonging to each MR in each image by extracting them based on the “date”:
MR_now <- all_img[4460]
MR_now[1] "_set1_day3_20191002_001.rsml"head(final)
super_temp <- subset(final, final$image %in% MR_now)
super_tempThen - we need to create some info about the length of individual zones - for this we need info on MR_length:
MR_length <- subset(only_MR, only_MR$image %in% MR_now)
MR_length <- MR_length$MRL
MR_length[1] 5.284145Length of Apical zone is basically equal to position of first lateral root Branched zone length is the position of last LR - position of first LR Basal zone is the MR length - position of last LR thus:
super_temp <- subset(super_temp, super_temp$root_ontology == unique(super_temp$root_ontology)[2])
super_tempApical <- min(super_temp$insertion_position)
Branched <- (max(super_temp$insertion_position) - Apical)
Basal <- MR_length - max(super_temp$insertion_position)
Apical[1] 0Branched[1] 2.728526Basal[1] 2.555619Another thing that I am interested in is the distribution of LRL across the MR and how quickly that decreases across the MRL
super_temp#super_temp$adj_position <- super_temp$insertion_position + 0.0000001
plot(super_temp$length ~ super_temp$insertion_position)
abline(lm(super_temp$length ~ super_temp$insertion_position))
model <- lm(super_temp$length ~ super_temp$insertion_position)
summary(model)
Call:
lm(formula = super_temp$length ~ super_temp$insertion_position)
Residuals:
Min 1Q Median 3Q Max
-1.27974 -0.14623 -0.03718 0.27756 0.93525
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7538 0.3756 4.67 0.00229 **
super_temp$insertion_position -0.5973 0.2254 -2.65 0.03293 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.648 on 7 degrees of freedom
Multiple R-squared: 0.5009, Adjusted R-squared: 0.4296
F-statistic: 7.024 on 1 and 7 DF, p-value: 0.03293model$coefficients (Intercept) super_temp$insertion_position
1.753818 -0.597283 LRL.dec <- model$coefficients[[2]]
LRL.dec.R2 <- summary(model)$r.squared
LRL.dec.R2[1] 0.5008563LRL.dec[1] -0.597283Then - we can calculate the % of LR across the MR length - by dividing the MR into 4 or 10 parts
super_temp$LR_in_10_perc <- 0
super_temp$LR_in_20_perc <- 0
super_temp$LR_in_30_perc <- 0
super_temp$LR_in_40_perc <- 0
super_temp$LR_in_50_perc <- 0
super_temp$LR_in_60_perc <- 0
super_temp$LR_in_70_perc <- 0
super_temp$LR_in_80_perc <- 0
super_temp$LR_in_90_perc <- 0
super_temp$LR_in_100_perc <- 0
for(e in 1:nrow(super_temp)){
if (super_temp$insertion_position[e] < (MR_length/10)){
super_temp$LR_in_10_perc[e] <- 1
} else {super_temp$LR_in_10_perc[e] <- 0}
if (super_temp$insertion_position[e] < 2*(MR_length/10) & super_temp$insertion_position[e] > (MR_length/10)){
super_temp$LR_in_20_perc[e] <- 1
} else {super_temp$LR_in_20_perc[e] <- 0}
if (super_temp$insertion_position[e] < 3*(MR_length/10) & super_temp$insertion_position[e] > 2*(MR_length/10)){
super_temp$LR_in_30_perc[e] <- 1
} else {super_temp$LR_in_30_perc[e] <- 0}
if (super_temp$insertion_position[e] < 4*(MR_length/10) & super_temp$insertion_position[e] > 3*(MR_length/10)){
super_temp$LR_in_40_perc[e] <- 1
} else {super_temp$LR_in_40_perc[e] <- 0}
if (super_temp$insertion_position[e] < 5*(MR_length/10) & super_temp$insertion_position[e] > 4*(MR_length/10)){
super_temp$LR_in_50_perc[e] <- 1
} else {super_temp$LR_in_50_perc[e] <- 0}
if (super_temp$insertion_position[e] < 6*(MR_length/10) & super_temp$insertion_position[e] > 5*(MR_length/10)){
super_temp$LR_in_60_perc[e] <- 1
} else {super_temp$LR_in_60_perc[e] <- 0}
if (super_temp$insertion_position[e] < 7*(MR_length/10) & super_temp$insertion_position[e] > 6*(MR_length/10)){
super_temp$LR_in_70_perc[e] <- 1
} else {super_temp$LR_in_70_perc[e] <- 0}
if (super_temp$insertion_position[e] < 8*(MR_length/10) & super_temp$insertion_position[e] > 7*(MR_length/10)){
super_temp$LR_in_80_perc[e] <- 1
} else {super_temp$LR_in_80_perc[e] <- 0}
if (super_temp$insertion_position[e] < 9*(MR_length/10) & super_temp$insertion_position[e] > 8*(MR_length/10)){
super_temp$LR_in_90_perc[e] <- 1
} else {super_temp$LR_in_90_perc[e] <- 0}
if (super_temp$insertion_position[e] < 10*(MR_length/10) & super_temp$insertion_position[e] > 9*(MR_length/10)){
super_temp$LR_in_100_perc[e] <- 1
} else {super_temp$LR_in_100_perc[e] <- 0}
}
super_tempso after classifying each LR to its respective portion of MR, we can then summarize them and calculate total LR length in this fragment:
super_temp$LRL_in_10_perc <- super_temp$length * super_temp$LR_in_10_perc
super_temp$LRL_in_20_perc <- super_temp$length * super_temp$LR_in_20_perc
super_temp$LRL_in_30_perc <- super_temp$length * super_temp$LR_in_30_perc
super_temp$LRL_in_40_perc <- super_temp$length * super_temp$LR_in_40_perc
super_temp$LRL_in_50_perc <- super_temp$length * super_temp$LR_in_50_perc
super_temp$LRL_in_60_perc <- super_temp$length * super_temp$LR_in_60_perc
super_temp$LRL_in_70_perc <- super_temp$length * super_temp$LR_in_70_perc
super_temp$LRL_in_80_perc <- super_temp$length * super_temp$LR_in_80_perc
super_temp$LRL_in_90_perc <- super_temp$length * super_temp$LR_in_90_perc
super_temp$LRL_in_100_perc <- super_temp$length * super_temp$LR_in_100_perc
LR_no_10_100 <- sum(super_temp$LR_in_10_perc)
LR_no_20_100 <- sum(super_temp$LR_in_20_perc)
LR_no_30_100 <- sum(super_temp$LR_in_30_perc)
LR_no_40_100 <- sum(super_temp$LR_in_40_perc)
LR_no_50_100 <- sum(super_temp$LR_in_50_perc)
LR_no_60_100 <- sum(super_temp$LR_in_60_perc)
LR_no_70_100 <- sum(super_temp$LR_in_70_perc)
LR_no_80_100 <- sum(super_temp$LR_in_80_perc)
LR_no_90_100 <- sum(super_temp$LR_in_90_perc)
LR_no_100_100 <- sum(super_temp$LR_in_100_perc)
LRL_10_100 <- sum(super_temp$LRL_in_10_perc)
LRL_20_100 <- sum(super_temp$LRL_in_20_perc)
LRL_30_100 <- sum(super_temp$LRL_in_30_perc)
LRL_40_100 <- sum(super_temp$LRL_in_40_perc)
LRL_50_100 <- sum(super_temp$LRL_in_50_perc)
LRL_60_100 <- sum(super_temp$LRL_in_60_perc)
LRL_70_100 <- sum(super_temp$LRL_in_70_perc)
LRL_80_100 <- sum(super_temp$LRL_in_80_perc)
LRL_90_100 <- sum(super_temp$LRL_in_90_perc)
LRL_100_100 <- sum(super_temp$LRL_in_100_perc)OK - last but not least - we should calculate the center of gravity for LR:
super_temp$momentum <- (super_temp$insertion_position * super_temp$length)
all_momentum <- sum(super_temp$momentum)
all_momentum[1] 6.590597all_length <- sum(super_temp$length)
all_length[1] 8.455498CoG <- (all_momentum / all_length)
CoG[1] 0.7794452Finally - let’s add all these values into our MR_only table
First we need to create needed columns:
only_MR$Apical <- 0
only_MR$Branched <- 0
only_MR$Basal <- 0
only_MR$Apical_perc <- 0
only_MR$Branched_perc <- 0
only_MR$Basal_perc <- 0
only_MR$LR_no_10_100 <- 0
only_MR$LR_no_20_100 <- 0
only_MR$LR_no_30_100 <- 0
only_MR$LR_no_40_100 <- 0
only_MR$LR_no_50_100 <- 0
only_MR$LR_no_60_100 <- 0
only_MR$LR_no_70_100 <- 0
only_MR$LR_no_80_100 <- 0
only_MR$LR_no_90_100 <- 0
only_MR$LR_no_100_100 <- 0
only_MR$LRL_10_100 <- 0
only_MR$LRL_20_100 <- 0
only_MR$LRL_30_100 <- 0
only_MR$LRL_40_100 <- 0
only_MR$LRL_50_100 <- 0
only_MR$LRL_60_100 <- 0
only_MR$LRL_70_100 <- 0
only_MR$LRL_80_100 <- 0
only_MR$LRL_90_100 <- 0
only_MR$LRL_100_100 <- 0
only_MR$CoG <- 0
only_MR$LRL.dec <- 0
only_MR$LRL.dec.R2 <- 0
head(only_MR)then add values to these columns:
only_MR$Apical[1] <- Apical
only_MR$Branched[1] <- Branched
only_MR$Basal[1] <- Basal
only_MR$Apical_perc[1] <- (Apical / MR_length)
only_MR$Branched_perc[1] <- (Branched / MR_length)
only_MR$Basal_perc[1] <- (Basal / MR_length)
only_MR$LR_no_10_100[1] <- LR_no_10_100
only_MR$LR_no_20_100[1] <- LR_no_20_100
only_MR$LR_no_30_100[1] <- LR_no_30_100
only_MR$LR_no_40_100[1] <- LR_no_40_100
only_MR$LR_no_50_100[1] <- LR_no_50_100
only_MR$LR_no_60_100[1] <- LR_no_60_100
only_MR$LR_no_70_100[1] <- LR_no_70_100
only_MR$LR_no_80_100[1] <- LR_no_80_100
only_MR$LR_no_90_100[1] <- LR_no_90_100
only_MR$LR_no_100_100[1] <- LR_no_100_100
only_MR$LRL_10_100[1] <- LRL_10_100
only_MR$LRL_20_100[1] <- LRL_20_100
only_MR$LRL_30_100[1] <- LRL_30_100
only_MR$LRL_40_100[1] <- LRL_40_100
only_MR$LRL_50_100[1] <- LRL_50_100
only_MR$LRL_60_100[1] <- LRL_60_100
only_MR$LRL_70_100[1] <- LRL_70_100
only_MR$LRL_80_100[1] <- LRL_80_100
only_MR$LRL_90_100[1] <- LRL_90_100
only_MR$LRL_100_100[1] <- LRL_100_100
only_MR$CoG[1] <- CoG
only_MR$LRL.dec[1] <- LRL.dec
only_MR$LRL.dec.R2[1] <- LRL.dec.R2
head(only_MR)cool - looks good. Let’s loop it:
dim(final)[1] 41269 15length(all_img)[1] 4460for(i in c(2:4460)){
MR_now <- all_img[i]
super_temp <- subset(final, final$image %in% MR_now)
MR_length <- subset(only_MR, only_MR$image %in% MR_now)
MR_length <- MR_length$MRL
super_temp <- subset(super_temp, super_temp$root_ontology == unique(super_temp$root_ontology)[2])
Apical <- min(super_temp$insertion_position)
Branched <- (max(super_temp$insertion_position) - Apical)
Basal <- MR_length - max(super_temp$insertion_position)
super_temp$LR_in_10_perc <- 0
super_temp$LR_in_20_perc <- 0
super_temp$LR_in_30_perc <- 0
super_temp$LR_in_40_perc <- 0
super_temp$LR_in_50_perc <- 0
super_temp$LR_in_60_perc <- 0
super_temp$LR_in_70_perc <- 0
super_temp$LR_in_80_perc <- 0
super_temp$LR_in_90_perc <- 0
super_temp$LR_in_100_perc <- 0
for(e in 1:nrow(super_temp)){
if (super_temp$insertion_position[e] < (MR_length/10)){
super_temp$LR_in_10_perc[e] <- 1
} else {super_temp$LR_in_10_perc[e] <- 0}
if (super_temp$insertion_position[e] < 2*(MR_length/10) & super_temp$insertion_position[e] > (MR_length/10)){
super_temp$LR_in_20_perc[e] <- 1
} else {super_temp$LR_in_20_perc[e] <- 0}
if (super_temp$insertion_position[e] < 3*(MR_length/10) & super_temp$insertion_position[e] > 2*(MR_length/10)){
super_temp$LR_in_30_perc[e] <- 1
} else {super_temp$LR_in_30_perc[e] <- 0}
if (super_temp$insertion_position[e] < 4*(MR_length/10) & super_temp$insertion_position[e] > 3*(MR_length/10)){
super_temp$LR_in_40_perc[e] <- 1
} else {super_temp$LR_in_40_perc[e] <- 0}
if (super_temp$insertion_position[e] < 5*(MR_length/10) & super_temp$insertion_position[e] > 4*(MR_length/10)){
super_temp$LR_in_50_perc[e] <- 1
} else {super_temp$LR_in_50_perc[e] <- 0}
if (super_temp$insertion_position[e] < 6*(MR_length/10) & super_temp$insertion_position[e] > 5*(MR_length/10)){
super_temp$LR_in_60_perc[e] <- 1
} else {super_temp$LR_in_60_perc[e] <- 0}
if (super_temp$insertion_position[e] < 7*(MR_length/10) & super_temp$insertion_position[e] > 6*(MR_length/10)){
super_temp$LR_in_70_perc[e] <- 1
} else {super_temp$LR_in_70_perc[e] <- 0}
if (super_temp$insertion_position[e] < 8*(MR_length/10) & super_temp$insertion_position[e] > 7*(MR_length/10)){
super_temp$LR_in_80_perc[e] <- 1
} else {super_temp$LR_in_80_perc[e] <- 0}
if (super_temp$insertion_position[e] < 9*(MR_length/10) & super_temp$insertion_position[e] > 8*(MR_length/10)){
super_temp$LR_in_90_perc[e] <- 1
} else {super_temp$LR_in_90_perc[e] <- 0}
if (super_temp$insertion_position[e] < 10*(MR_length/10) & super_temp$insertion_position[e] > 9*(MR_length/10)){
super_temp$LR_in_100_perc[e] <- 1
} else {super_temp$LR_in_100_perc[e] <- 0}
}
super_temp$LRL_in_10_perc <- super_temp$length * super_temp$LR_in_10_perc
super_temp$LRL_in_20_perc <- super_temp$length * super_temp$LR_in_20_perc
super_temp$LRL_in_30_perc <- super_temp$length * super_temp$LR_in_30_perc
super_temp$LRL_in_40_perc <- super_temp$length * super_temp$LR_in_40_perc
super_temp$LRL_in_50_perc <- super_temp$length * super_temp$LR_in_50_perc
super_temp$LRL_in_60_perc <- super_temp$length * super_temp$LR_in_60_perc
super_temp$LRL_in_70_perc <- super_temp$length * super_temp$LR_in_70_perc
super_temp$LRL_in_80_perc <- super_temp$length * super_temp$LR_in_80_perc
super_temp$LRL_in_90_perc <- super_temp$length * super_temp$LR_in_90_perc
super_temp$LRL_in_100_perc <- super_temp$length * super_temp$LR_in_100_perc
LR_no_10_100 <- sum(super_temp$LR_in_10_perc)
LR_no_20_100 <- sum(super_temp$LR_in_20_perc)
LR_no_30_100 <- sum(super_temp$LR_in_30_perc)
LR_no_40_100 <- sum(super_temp$LR_in_40_perc)
LR_no_50_100 <- sum(super_temp$LR_in_50_perc)
LR_no_60_100 <- sum(super_temp$LR_in_60_perc)
LR_no_70_100 <- sum(super_temp$LR_in_70_perc)
LR_no_80_100 <- sum(super_temp$LR_in_80_perc)
LR_no_90_100 <- sum(super_temp$LR_in_90_perc)
LR_no_100_100 <- sum(super_temp$LR_in_100_perc)
LRL_10_100 <- sum(super_temp$LRL_in_10_perc)
LRL_20_100 <- sum(super_temp$LRL_in_20_perc)
LRL_30_100 <- sum(super_temp$LRL_in_30_perc)
LRL_40_100 <- sum(super_temp$LRL_in_40_perc)
LRL_50_100 <- sum(super_temp$LRL_in_50_perc)
LRL_60_100 <- sum(super_temp$LRL_in_60_perc)
LRL_70_100 <- sum(super_temp$LRL_in_70_perc)
LRL_80_100 <- sum(super_temp$LRL_in_80_perc)
LRL_90_100 <- sum(super_temp$LRL_in_90_perc)
LRL_100_100 <- sum(super_temp$LRL_in_100_perc)
super_temp$momentum <- (super_temp$insertion_position * super_temp$length)
all_momentum <- sum(super_temp$momentum)
all_length <- sum(super_temp$length)
CoG <- (all_momentum / all_length)
if(dim(super_temp)[1] > 2){
model <- lm(super_temp$length ~ super_temp$insertion_position)
LRL.dec <- model$coefficients[[2]]
LRL.dec.R2 <- summary(model)$r.squared
} else {
LRL.dec <- "n.a."
LRL.dec.R2 <- "n.a."
}
only_MR$Apical[i] <- Apical
only_MR$Branched[i] <- Branched
only_MR$Basal[i] <- Basal
only_MR$Apical_perc[i] <- (Apical / MR_length)
only_MR$Branched_perc[i] <- (Branched / MR_length)
only_MR$Basal_perc[i] <- (Basal / MR_length)
only_MR$LR_no_10_100[i] <- LR_no_10_100
only_MR$LR_no_20_100[i] <- LR_no_20_100
only_MR$LR_no_30_100[i] <- LR_no_30_100
only_MR$LR_no_40_100[i] <- LR_no_40_100
only_MR$LR_no_50_100[i] <- LR_no_50_100
only_MR$LR_no_60_100[i] <- LR_no_60_100
only_MR$LR_no_70_100[i] <- LR_no_70_100
only_MR$LR_no_80_100[i] <- LR_no_80_100
only_MR$LR_no_90_100[i] <- LR_no_90_100
only_MR$LR_no_100_100[i] <- LR_no_100_100
only_MR$LRL_10_100[i] <- LRL_10_100
only_MR$LRL_20_100[i] <- LRL_20_100
only_MR$LRL_30_100[i] <- LRL_30_100
only_MR$LRL_40_100[i] <- LRL_40_100
only_MR$LRL_50_100[i] <- LRL_50_100
only_MR$LRL_60_100[i] <- LRL_60_100
only_MR$LRL_70_100[i] <- LRL_70_100
only_MR$LRL_80_100[i] <- LRL_80_100
only_MR$LRL_90_100[i] <- LRL_90_100
only_MR$LRL_100_100[i] <- LRL_100_100
only_MR$CoG[i] <- CoG
only_MR$LRL.dec[i] <- LRL.dec
only_MR$LRL.dec.R2[i] <- LRL.dec.R2
}
let’s have a look at the entire table:
head(only_MR)tail(only_MR)then- we need to fuse all the main roots that dont own LR
Make sure that the data structure is the same as for the only_MR file
head(no_LR_MR)let’s add missing collumns:
no_LR_MR$Apical <- "n.a."
no_LR_MR$Branched <- "n.a."
no_LR_MR$Basal <- "n.a."
no_LR_MR$Apical_perc <- "n.a."
no_LR_MR$Branched_perc <- "n.a."
no_LR_MR$Basal_perc <- "n.a."
no_LR_MR$LR_no_10_100 <- 0
no_LR_MR$LR_no_20_100 <- 0
no_LR_MR$LR_no_30_100 <- 0
no_LR_MR$LR_no_40_100 <- 0
no_LR_MR$LR_no_50_100 <- 0
no_LR_MR$LR_no_60_100 <- 0
no_LR_MR$LR_no_70_100 <- 0
no_LR_MR$LR_no_80_100 <- 0
no_LR_MR$LR_no_90_100 <- 0
no_LR_MR$LR_no_100_100 <- 0
no_LR_MR$LRL_10_100 <- 0
no_LR_MR$LRL_20_100 <- 0
no_LR_MR$LRL_30_100 <- 0
no_LR_MR$LRL_40_100 <- 0
no_LR_MR$LRL_50_100 <- 0
no_LR_MR$LRL_60_100 <- 0
no_LR_MR$LRL_70_100 <- 0
no_LR_MR$LRL_80_100 <- 0
no_LR_MR$LRL_90_100 <- 0
no_LR_MR$LRL_100_100 <- 0
no_LR_MR$CoG <- 0
no_LR_MR$LRL.dec <- "n.a."
no_LR_MR$LRL.dec.R2 <- "n.a."finally - let’s fuse the two files together:
colnames(only_MR) [1] "image" "root_name" "root" "experiment"
[5] "date" "genotype" "rep" "cond"
[9] "MRL" "n_child" "insertion_first_child" "insertion_last_child"
[13] "image_id" "Apical" "Branched" "Basal"
[17] "Apical_perc" "Branched_perc" "Basal_perc" "LR_no_10_100"
[21] "LR_no_20_100" "LR_no_30_100" "LR_no_40_100" "LR_no_50_100"
[25] "LR_no_60_100" "LR_no_70_100" "LR_no_80_100" "LR_no_90_100"
[29] "LR_no_100_100" "LRL_10_100" "LRL_20_100" "LRL_30_100"
[33] "LRL_40_100" "LRL_50_100" "LRL_60_100" "LRL_70_100"
[37] "LRL_80_100" "LRL_90_100" "LRL_100_100" "CoG"
[41] "LRL.dec" "LRL.dec.R2" colnames(no_LR_MR) [1] "image" "root_name" "root" "experiment"
[5] "date" "genotype" "rep" "cond"
[9] "MRL" "n_child" "insertion_first_child" "insertion_last_child"
[13] "Apical" "Branched" "Basal" "Apical_perc"
[17] "Branched_perc" "Basal_perc" "LR_no_10_100" "LR_no_20_100"
[21] "LR_no_30_100" "LR_no_40_100" "LR_no_50_100" "LR_no_60_100"
[25] "LR_no_70_100" "LR_no_80_100" "LR_no_90_100" "LR_no_100_100"
[29] "LRL_10_100" "LRL_20_100" "LRL_30_100" "LRL_40_100"
[33] "LRL_50_100" "LRL_60_100" "LRL_70_100" "LRL_80_100"
[37] "LRL_90_100" "LRL_100_100" "CoG" "LRL.dec"
[41] "LRL.dec.R2" only_MR <- only_MR[,c(1:12,14:42)]all_MR <- rbind(only_MR, no_LR_MR)
head(all_MR)dim(all_MR)[1] 5511 41length(unique(all_MR$root_name))[1] 1076length(unique(all_MR$image))[1] 5511length(unique(all_MR$image))/length(unique(all_MR$root_name))[1] 5.121747Let’s add some more interesting columns:
all_MR$LRL <- (all_MR$LRL_10_100 + all_MR$LRL_20_100 + all_MR$LRL_30_100 + all_MR$LRL_40_100 + all_MR$LRL_50_100 + all_MR$LRL_60_100 + all_MR$LRL_70_100 + all_MR$LRL_80_100 + all_MR$LRL_90_100 + all_MR$LRL_100_100)
all_MR$LRno <- (all_MR$LR_no_10_100 + all_MR$LR_no_20_100 + all_MR$LR_no_30_100 + all_MR$LR_no_40_100 + all_MR$LR_no_50_100 + all_MR$LR_no_60_100 + all_MR$LR_no_70_100 + all_MR$LR_no_80_100 + all_MR$LR_no_90_100 + all_MR$LR_no_100_100)
all_MR$aLRL <- all_MR$LRL / all_MR$LRno
all_MR$TRS <- all_MR$MRL + all_MR$LRL
head(all_MR)data visualization
OK - now we have all the traits calculated - but we are still missing the DAY information - as in DAY after stress. So let’s calculate it per experiment:
head(all_MR)unique(all_MR[,c("experiment", "date")])based on the above - we have the following
day Big01 Big02 Big03 Big04 day 0 - 20190909 20190923 20190930 20191008 day 1 - 20190910 20190924 20191001 20191009 day 2 - 20190911 20190925 20191002 20191010 day 3 - 20190912 20190926 20191003 20191011 day 4 - 20190913 20190927 20191004 20191012
So now we have to do conditional formating for DAS (Days After Stress):
all_MR$DAS <- all_MR$date
all_MR$DAS <- gsub("20190909", "0", all_MR$DAS)
all_MR$DAS <- gsub("20190923", "0", all_MR$DAS)
all_MR$DAS <- gsub("20190930", "0", all_MR$DAS)
all_MR$DAS <- gsub("20191008", "0", all_MR$DAS)
all_MR$DAS <- gsub("20190910", "1", all_MR$DAS)
all_MR$DAS <- gsub("20190924", "1", all_MR$DAS)
all_MR$DAS <- gsub("20191001", "1", all_MR$DAS)
all_MR$DAS <- gsub("20191009", "1", all_MR$DAS)
all_MR$DAS <- gsub("20190911", "2", all_MR$DAS)
all_MR$DAS <- gsub("20190925", "2", all_MR$DAS)
all_MR$DAS <- gsub("20191002", "2", all_MR$DAS)
all_MR$DAS <- gsub("20191010", "2", all_MR$DAS)
all_MR$DAS <- gsub("20190912", "3", all_MR$DAS)
all_MR$DAS <- gsub("20190926", "3", all_MR$DAS)
all_MR$DAS <- gsub("20191003", "3", all_MR$DAS)
all_MR$DAS <- gsub("20191011", "3", all_MR$DAS)
all_MR$DAS <- gsub("20190913", "4", all_MR$DAS)
all_MR$DAS <- gsub("20190927", "4", all_MR$DAS)
all_MR$DAS <- gsub("20191004", "4", all_MR$DAS)
all_MR$DAS <- gsub("20191012", "4", all_MR$DAS)
unique(all_MR[,c("experiment", "DAS")])before we go any further - let’s clean up this data and add whatever else missing traits:
colnames(all_MR) [1] "image" "root_name" "root" "experiment"
[5] "date" "genotype" "rep" "cond"
[9] "MRL" "n_child" "insertion_first_child" "insertion_last_child"
[13] "Apical" "Branched" "Basal" "Apical_perc"
[17] "Branched_perc" "Basal_perc" "LR_no_10_100" "LR_no_20_100"
[21] "LR_no_30_100" "LR_no_40_100" "LR_no_50_100" "LR_no_60_100"
[25] "LR_no_70_100" "LR_no_80_100" "LR_no_90_100" "LR_no_100_100"
[29] "LRL_10_100" "LRL_20_100" "LRL_30_100" "LRL_40_100"
[33] "LRL_50_100" "LRL_60_100" "LRL_70_100" "LRL_80_100"
[37] "LRL_90_100" "LRL_100_100" "CoG" "LRL.dec"
[41] "LRL.dec.R2" "LRL" "LRno" "aLRL"
[45] "TRS" "DAS" dim(all_MR)[1] 5511 46all_MR2 <- all_MR[,c(1:4, 46, 6:9, 13:45)]
head(all_MR2)all_MR2$MRL.p.TRS <- all_MR2$MRL / all_MR2$TRS
all_MR2$aLRL.p.TRS <- all_MR2$aLRL / all_MR2$TRS
all_MR2$LRL_10_perc <- all_MR2$LRL_10_100 / all_MR2$LRL
all_MR2$LRL_20_perc <- all_MR2$LRL_20_100 / all_MR2$LRL
all_MR2$LRL_30_perc <- all_MR2$LRL_30_100 / all_MR2$LRL
all_MR2$LRL_40_perc <- all_MR2$LRL_40_100 / all_MR2$LRL
all_MR2$LRL_50_perc <- all_MR2$LRL_50_100 / all_MR2$LRL
all_MR2$LRL_60_perc <- all_MR2$LRL_60_100 / all_MR2$LRL
all_MR2$LRL_70_perc <- all_MR2$LRL_70_100 / all_MR2$LRL
all_MR2$LRL_80_perc <- all_MR2$LRL_80_100 / all_MR2$LRL
all_MR2$LRL_90_perc <- all_MR2$LRL_90_100 / all_MR2$LRL
all_MR2$LRL_100_perc <- all_MR2$LRL_100_100 / all_MR2$LRL
all_MR2$LRno_10_perc <- all_MR2$LR_no_10_100 / all_MR2$LRno
all_MR2$LRno_20_perc <- all_MR2$LR_no_20_100 / all_MR2$LRno
all_MR2$LRno_30_perc <- all_MR2$LR_no_30_100 / all_MR2$LRno
all_MR2$LRno_40_perc <- all_MR2$LR_no_40_100 / all_MR2$LRno
all_MR2$LRno_50_perc <- all_MR2$LR_no_50_100 / all_MR2$LRno
all_MR2$LRno_60_perc <- all_MR2$LR_no_60_100 / all_MR2$LRno
all_MR2$LRno_70_perc <- all_MR2$LR_no_70_100 / all_MR2$LRno
all_MR2$LRno_80_perc <- all_MR2$LR_no_80_100 / all_MR2$LRno
all_MR2$LRno_90_perc <- all_MR2$LR_no_90_100 / all_MR2$LRno
all_MR2$LRno_100_perc <- all_MR2$LR_no_100_100 / all_MR2$LRno
head(all_MR2)Just before we start plotting the data - let’s also check the genotype information whether this all makes sense and if we are not missing any info:
unique(all_MR2$genotype) [1] " 189" " 292" " 213" " 212" " 307" " 282" " 298" " 301" " 286" " 214" " 194" " 294" " 285" " 293"
[15] " 284" " 278" " 300" " 201" " 279" " 280" " 299" " 200" " 211" " 308" " 197" " 287" " 196" " 220"
[29] " 207" " 290" " 198" " 195" " 289" " 288" " 291" " 2800" " 199" " 277" " 283" " 306" " 235" " 192"
[43] " 147" " 252" " 166" " 269" " 064" " 253" " 193" " 146" " 141" " 251" " 139" " 169" " 258" " 144"
[57] " 138" " 061" " 065" " 170" " 142" " 140" " 168" " 272" " 165" " 255" " 260" " 264" " 262" " 261"
[71] " 167" " 060" " 066" " 136" " 256" " 143" " 171" " 082" " 083" " 191" " 259" " 062" " 172" " 063"
[85] " 270" " 268" " 267" " 234" " 245" " 173" " 236" " 084" " 229" " 241" " 231" " 227" " 176" " 087"
[99] " 091" " 090" " 085" " 228" " 230" " 175" " 250" " 222" " 239" " 233" " 274" " 088" " 271" " 240"
[113] " 092" " 089" " 247" " 038" " 248" " 086" " 273" " 244" " 093" " 174" " 249" " 237" " 221" " 225"
[127] " 103" " 028" " 098" " 055" " 075" " 059" " 101" " 034" " 002" " 030" " 056" " 001" " 102" " 012"
[141] " 076" " 079" " 104" " 081" " 096" " 058" " 099" " 105" " 040" " 035" " 094" " 074" " 032" " 022"
[155] " 003" " 095" " 009" " 106" " 057" " 033" " 029" " 013" " 031" " 007" " 008" " 006" " 080" " 005"
[169] " 036" " 011" " 078" " 010" " 097" " 037" unique(all_MR$root_name) [1] " 189_3_C" " 292_3_S" " 213_3_S" " 212_1_S" " 307_2_C" " 282_3_C" " 298_3_S" " 301_1_C" " 286_1_S"
[10] " 213_2_C" " 214_1_C" " 286_2_C" " 194_1_S" " 212_1_C" " 294_2_C" " 213_1_C" " 285_2_S" " 293_2_C"
[19] " 284_2_S" " 285_3_S" " 278_1_C" " 300_1_S" " 214_3_C" " 292_1_C" " 201_2_S" " 279_1_S" " 293_3_S"
[28] " 280_1_S" " 194_3_S" " 294_3_S" " 299_2_C" " 298_3_C" " 282_1_C" " 285_3_C" " 307_3_C" " 200_1_S"
[37] " 211_3_S" " 308_3_C" " 308_2_C" " 197_3_S" " 287_1_C" " 301_1_S" " 196_1_C" " 299_1_C" " 220_3_C"
[46] " 293_2_S" " 207_1_C" " 280_1_C" " 220_1_C" " 213_2_S" " 194_3_C" " 194_1_C" " 282_2_S" " 290_3_C"
[55] " 196_2_C" " 300_2_C" " 198_2_C" " 211_1_C" " 285_2_C" " 197_1_C" " 292_2_C" " 195_2_S" " 294_2_S"
[64] " 289_2_S" " 282_2_C" " 299_1_S" " 300_3_C" " 197_2_C" " 298_2_C" " 288_3_C" " 213_3_C" " 201_3_C"
[73] " 291_3_C" " 2800_3_S" " 196_1_S" " 279_1_C" " 278_3_S" " 213_1_S" " 294_1_C" " 294_1_S" " 195_1_C"
[82] " 199_3_C" " 290_1_C" " 197_3_C" " 212_2_S" " 211_2_S" " 211_2_C" " 199_1_C" " 214_1_S" " 277_1_S"
[91] " 197_1_S" " 292_3_C" " 291_2_C" " 207_2_C" " 214_2_C" " 285_1_S" " 194_2_C" " 278_2_C" " 291_1_C"
[100] " 212_3_S" " 300_3_S" " 284_1_C" " 293_1_S" " 283_1_C" " 211_3_C" " 283_3_S" " 286_3_C" " 277_2_C"
[109] " 279_3_S" " 283_1_S" " 298_1_S" " 211_1_S" " 197_2_S" " 288_1_S" " 300_2_S" " 294_3_C" " 198_3_S"
[118] " 201_1_C" " 288_2_C" " 207_3_S" " 288_1_C" " 280_3_C" " 306_1_C" " 212_2_C" " 212_3_C" " 207_3_C"
[127] " 286_2_S" " 201_1_S" " 277_1_C" " 195_3_C" " 283_2_C" " 288_3_S" " 291_2_S" " 290_2_C" " 298_1_C"
[136] " 284_1_S" " 308_3_S" " 284_2_C" " 290_1_S" " 195_3_S" " 214_3_S" " 200_3_S" " 200_2_S" " 195_2_C"
[145] " 200_1_C" " 279_3_C" " 301_2_C" " 288_2_S" " 301_3_S" " 308_1_C" " 279_2_C" " 306_2_C" " 286_1_C"
[154] " 291_1_S" " 280_2_C" " 199_2_C" " 235_1_C" " 290_2_S" " 195_1_S" " 283_3_C" " 290_3_S" " 199_3_S"
[163] " 293_3_C" " 198_1_S" " 198_2_S" " 292_2_S" " 198_1_C" " 200_2_C" " 286_3_S" " 200_3_C" " 201_2_C"
[172] " 293_4_C" " 308_2_S" " 291_3_S" " 282_3_S" " 201_3_S" " 278_3_C" " 283_2_S" " 287_3_C" " 278_2_S"
[181] " 282_1_S" " 199_2_S" " 278_1_S" " 307_2_S" " 307_1_S" " 299_3_C" " 199_1_S" " 307_3_S" " 220_2_C"
[190] " 287_2_S" " 301_3" " 287_2_C" " 307_1_C" " 220_1_S" " 306_3_C" " 299_2_S" " 308_1_S" " 277_2_S"
[199] " 192_3_C" " 207_1_S" " 301_2_S" " 194_2_S" " 214_2_S" " 306_1_S" " 207_2_S" " 306_3_S" " 287_3_S"
[208] " 147_3_C" " 252_1_S" " 166_1_C" " 269_3_C" " 064_1_C" " 253_2_S" " 193_1_S" " 146_3_C" " 141_2_C"
[217] " 251_3_S" " 139_3_S" " 169_1_S" " 258_3_S" " 192_1_C" " 144_2_C" " 147_2_C" " 253_3_S" " 138_3_S"
[226] " 146_2_C" " 193_1_C" " 061_2_C" " 065_3_S" " 170_3_C" " 141_1_C" " 144_1_C" " 142_4_S" " 139_1_S"
[235] " 140_3_C" " 168_3_S" " 272_2_C" " 138_4_C" " 147_1_S" " 269_3_S" " 165_1_S" " 144_2_S" " 255_1_S"
[244] " 138_1_C" " 272_3_S" " 138_4_S" " 251_2_C" " 269_1_C" " 260_2_C" " 264_2_S" " 262_3_C" " 261_1_S"
[253] " 192_3_S" " 065_2_S" " 168_3_C" " 260_1_C" " 167_1_S" " 166_3_C" " 253_2_C" " 142_3_C" " 060_3_S"
[262] " 165_2_C" " 262_2_C" " 066_2_C" " 136_3_C" " 142_3_S" " 140_2_S" " 256_2_S" " 139_2_C" " 143_2_C"
[271] " 262_1_C" " 251_3_C" " 138_2_C" " 165_1_C" " 139_2_S" " 166_1_S" " 171_3_S" " 256_1_C" " 146_1_C"
[280] " 264_1_C" " 136_2_C" " 065_3_C" " 170_2_C" " 146_2_S" " 192_2_C" " 255_2_C" " 147_1_C" " 140_2_C"
[289] " 169_2_C" " 146_3_S" " 169_3_S" " 061_1_C" " 061_3_C" " 262_4_C" " 252_2_S" " 141_3_C" " 140_3_S"
[298] " 166_3_S" " 082_3_C" " 136_1_C" " 252_3_C" " 083_3_C" " 165_3_C" " 272_3_C" " 193_3_C" " 141_2_S"
[307] " 140_1_C" " 142_2_C" " 191_2_C" " 171_2_C" " 144_3_C" " 193_2_S" " 144_1_S" " 272_1_C" " 259_3_C"
[316] " 138_1_S" " 062_1_C" " 147_2_S" " 170_2_S" " 172_1_C" " 136_2_S" " 170_1_C" " 260_3_C" " 063_1_S"
[325] " 060_2_S" " 142_4_C" " 256_3_C" " 256_2_C" " 269_2_S" " 065_1_C" " 138_2_S" " 171_2_S" " 142_1_S"
[334] " 193_2_C" " 253_1_C" " 253_1_S" " 259_2_S" " 065_2_C" " 169_2_S" " 168_2_C" " 142_2_S" " 171_1_C"
[343] " 060_3_C" " 192_1_S" " 171_1_S" " 261_2_C" " 169_1_C" " 191_1_C" " 258_2_C" " 063_1_C" " 258_3_C"
[352] " 261_3_C" " 270_2_C" " 140_1_S" " 252_1_C" " 083_2_C" " 264_3_C" " 082_2_C" " 258_1_C" " 169_3_C"
[361] " 272_2_S" " 168_1_C" " 255_1_C" " 256_3_S" " 138_3_C" " 139_3_C" " 061_3_S" " 136_3_S" " 082_1_C"
[370] " 262_4_S" " 253_3_C" " 191_3_C" " 272_1_S" " 268_2_C" " 192_2_S" " 144_3_S" " 259_1_C" " 172_2_C"
[379] " 136_1_S" " 147_3_S" " 142_1_C" " 060_1_C" " 083_1_C" " 267_2_C" " 066_1_C" " 252_3_S" " 146_1_S"
[388] " 264_2_C" " 268_3_C" " 262_1_S" " 251_1_C" " 165_3_S" " 268_3_S" " 269_1_S" " 269_2_C" " 171_3_C"
[397] " 165_2_S" " 166_2_C" " 260_1_S" " 060_2_C" " 139_1_C" " 172_3_C" " 270_1_C" " 060_1_S" " 260_2_S"
[406] " 261_1_C" " 264_1_S" " 261_2_S" " 260_3_S" " 172_1_S" " 191_1_S" " 251_2_S" " 255_2_S" " 258_1_S"
[415] " 267_1_C" " 065_1_S" " 141_1_S" " 143_1_C" " 268_2_S" " 062_2_S" " 256_1_S" " 262_3_S" " 261_3_S"
[424] " 259_2_C" " 270_2_S" " 191_3_S" " 141_3_S" " 061_2_S" " 066_1_S" " 252_2_C" " 168_2_S" " 082_3_S"
[433] " 264_3_S" " 259_3_S" " 143_1_S" " 193_3_S" " 167_1_C" " 259_1_S" " 191_2_S" " 262_2_S" " 170_3_S"
[442] " 267_1_S" " 066_2_S" " 061_1_S" " 168_1_S" " 172_3_S" " 064_1_S" " 082_2_S" " 270_1_S" " 166_2_S"
[451] " 172_2_S" " 064_2_S" " 251_1_S" " 143_2_S" " 258_2_S" " 083_2_S" " 083_3_S" " 268_1_S" " 234_1_C"
[460] " 245_3_S" " 173_4_C" " 236_3_S" " 084_3_C" " 229_4_C" " 245_1_S" " 236_4_C" " 241_1_C" " 231_2_S"
[469] " 245_4_S" " 227_1_C" " 176_1_C" " 229_4_S" " 235_2_S" " 087_1_S" " 091_4_S" " 090_3_C" " 091_4_C"
[478] " 085_2_S" " 228_2_C" " 230_3_s" " 175_4_C" " 250_1_C" " 222_2_S" " 229_2_S" " 239_4_C" " 229_3_S"
[487] " 233_1_C" " 250_4_S" " 274_2_C" " 088_1_C" " 230_1_S" " 234_2_C" " 271_2_S" " 173_1_C" " 240_2_S"
[496] " 092_4_C" " 084_1_S" " 084_1_C" " 229_3_C" " 233_3_S" " 236_1_S" " 239_4_S" " 236_2_S" " 173_3_C"
[505] " 233_2_C" " 089_3_C" " 234_2_S" " 241_2_C" " 084_3_S" " 247_1_S" " 245_3_C" " 089_1_" " 247_3_C"
[514] " 239_3_C" " 038_2_S" " 250_3_S" " 247_2_C" " 228_4_C" " 248_4_C" " 086_3_C" " 231_2_C" " 173_1_S"
[523] " 085_3_S" " 086_1_C" " 085_2_C" " 228_2_S" " 084_2_S" " 231_3_C" " 273_3_C" " 175_3_C" " 176_2_S"
[532] " 091_2_S" " 271_1_C" " 084_4_C" " 173_4_S" " 274_4_C" " 239_1_S" " 236_2_C" " 173_2_C" " 230_2_S"
[541] " 092_1_C" " 250_4_C" " 240_3_C" " 176_4_C" " 092_3_C" " 244_3_C" " 176_2_C" " 240_2_C" " 274_1_S"
[550] " 088_1_S" " 240_1_S" " 088_3_C" " 092_2_C" " 086_1_S" " 089_2_C" " 092_3_S" " 091_3_S" " 093_3_C"
[559] " 229_1_S" " 236_3_C" " 087_3_C" " 092_2_S" " 239_2_S" " 248_2_C" " 229_1_C" " 250_3_C" " 038_3_C"
[568] " 233_3_C" " 230_1_C" " 236_4_S" " 175_2_C" " 271_4_S" " 228_3_C" " 093_1_S" " 173_2_S" " 087_4_C"
[577] " 090_2_C" " 273_1_C" " 084_4_S" " 239_2_C" " 233_4_S" " 271_2_C" " 175_3_S" " 092_1_S" " 233_1_S"
[586] " 174_4_C" " 038_1_C" " 087_1_C" " 092_4_S" " 231_3_S" " 176_3_C" " 247_4_C" " 248_4_S" " 245_2_S"
[595] " 240_3_S" " 241_3_C" " 245_1_C" " 085_1_C" " 174_2_S" " 089_4_C" " 087_2_C" " 273_3_S" " 250_2_C"
[604] " 271_3_C" " 273_4_C" " 228_3_S" " 248_2_S" " 249_1_S" " 228_1_C" " 271_3_S" " 236_1_C" " 089_3_S"
[613] " 229_2_C" " 273_4_S" " 085_3_C" " 175_4_S" " 244_2_C" " 089_4_S" " 271_4_C" " 087_3_S" " 250_2_S"
[622] " 274_2_S" " 174_2_C" " 274_3_S" " 241_3_S" " 233_4_C" " 245_2_C" " 173_3_S" " 086_2_C" " 088_3_S"
[631] " 093_3_S" " 240_1_C" " 091_1_C" " 093_1_C" " 227_3_C" " 176_3_S" " 064_3_S" " 237_1_C" " 244_1_S"
[640] " 174_3_C" " 274_1_C" " 247_2_S" " 227_2_C" " 230_3_S" " 239_1_C" " 093_2_C" " 273_2_C" " 084_2_C"
[649] " 093_4_C" " 038_1_S" " 174_1_C" " 241_2_S" " 038_3_S" " 298_2_S" " 248_1_C" " 274_3_C" " 089_2_S"
[658] " 222_1_S" " 274_4_S" " 090_1_C" " 064_3_C" " 175_1_C" " 230_3_C" " 247_1_C" " 237_2_C" " 237_4_C"
[667] " 250_1_S" " 239_3_S" " 271_1_S" " 087_4_S" " 227_2_S" " 221_2_C" " 087_2_S" " 086_3_S" " 174_3_S"
[676] " 176_4_S" " 228_1_S" " 175_2_S" " 222_2_C" " 088_2_C" " 086_2_S" " 237_4_S" " 089_1_S" " 091_3_C"
[685] " 234_3_C" " 038_2_C" " 227_1_S" " 174_4_S" " 233_2_S" " 241_1_S" " 248_3_C" " 222_1_C" " 093_4_S"
[694] " 230_2_C" " 240_4_C" " 237_3_C" " 221_1_C" " 234_1_S" " 244_3_S" " 176_1_S" " 235_1_S" " 225_2_C"
[703] " 248_1_S" " 273_2_S" " 091_1_S" " 244_1_C" " 091_2_C" " 248_3_S" " 249_1_C" " 247_3_S" " 175_1_S"
[712] " 244_2_S" " 090_1_S" " 093_2_S" " 225_1_C" " 085_1_S" " 249_2_C" " 249_3_C" " 064_2_C" " 231_1_C"
[721] " 090_3_S" " 249_2_S" " 249_3_S" " 240_4_S" " 237_2_S" " 273_1_S" " 237_1_s" " 174_1_S" " 090_2_S"
[730] " 088_2_S" " 221_1_S" " 247_4_S" " 228_4_S" " 234_3_S" " 227_3_S" " 237_3_S" " 231_1_S" " 103_4_S"
[739] " 028_3_S" " 098_1_S" " 055_2_S" " 075_3_C" " 059_4_C" " 101_1_S" " 034_1_C" " 002_1_C" " 030_2_S"
[748] " 056_3_S" " 001_3_C" " 102_4_C" " 012_3_C" " 076_1_C" " 098_1_C" " 079_1_C" " 104_3_S" " 081_1_C"
[757] " 101_2_C" " 096_1_C" " 101_2_S" " 058_4_C" " 028_4_C" " 099_2_S" " 103_3_C" " 079_4_C" " 030_1_S"
[766] " 105_4_S" " 040_1_C" " 035_4_C" " 094_3_C" " 074_4_C" " 032_4_S" " 056_2_C" " 103_1_S" " 103_1_C"
[775] " 022_4_C" " 102_2_S" " 105_3_C" " 081_2_S" " 003_3_S" " 056_2_S" " 095_1_S" " 009_3_S" " 002_2_S"
[784] " 106_1_S" " 098_3_C" " 040_3_S" " 103_2_S" " 075_2_C" " 001_4_S" " 009_2_S" " 057_1_C" " 028_3_C"
[793] " 033_2_C" " 012_1_S" " 098_3_S" " 094_2_C" " 029_3_S" " 074_3_C" " 028_1_S" " 013_3_C" " 031_1_S"
[802] " 105_1_S" " 094_2_S" " 007_2_C" " 075_3_S" " 013_2_S" " 007_1_C" " 008_1_S" " 057_1_S" " 007_3_C"
[811] " 059_3_C" " 076_3_C" " 012_3_S" " 008_3_C" " 007_1_S" " 094_3_S" " 099_4_C" " 057_3_C" " 099_1_C"
[820] " 035_2_S" " 029_4_S" " 030_2_C" " 012_2_S" " 103_4_C" " 105_3_S" " 007_2_S" " 012_4_S" " 013_1_C"
[829] " 032_1_C" " 009_2_C" " 001_1_C" " 057_2_S" " 098_4_S" " 006_3_C" " 080_1_C" " 079_2_S" " 056_3_C"
[838] " 095_4_S" " 040_2_C" " 001_2_S" " 003_3_C" " 031_2_C" " 074_4_S" " 035_2_C" " 106_2_C" " 001_2_C"
[847] " 104_2_S" " 040_4_C" " 005_3_C" " 059_1_C" " 079_3_S" " 096_2_S" " 009_1_C" " 032_2_C" " 057_4_S"
[856] " 055_2_C" " 030_3_C" " 095_4_C" " 081_3_C" " 081_4_S" " 103_3_S" " 059_2_C" " 099_3_C" " 031_4_C"
[865] " 038_4_C" " 006_2_C" " 102_3_C" " 057_2_C" " 104_3_C" " 057_4_C" " 029_3_C" " 036_2_C" " 058_1_C"
[874] " 057_3_S" " 030_1_C" " 032_3_S" " 011_1_C" " 078_1_C" " 035_1_S" " 029_1_C" " 101_3_S" " 055_1_S"
[883] " 008_1_C" " 011_3_C" " 001_1_S" " 055_3_C" " 003_2_S" " 105_4_C" " 104_4_S" " 031_4_S" " 102_4_S"
[892] " 013_2_C" " 074_2_S" " 075_1_S" " 008_3_S" " 104_1_S" " 011_4_C" " 058_2_C" " 099_4_S" " 106_2_S"
[901] " 075_1_C" " 009_1_S" " 080_2_C" " 099_2_C" " 074_1_S" " 002_1_S" " 011_3_S" " 105_2_C" " 080_3_S"
[910] " 055_3_S" " 028_1_C" " 008_2_S" " 035_3_C" " 011_2_C" " 094_1_C" " 013_3_S" " 055_1_C" " 005_2_C"
[919] " 005_4_C" " 104_2_C" " 056_1_C" " 101_4_S" " 079_4_S" " 012_2_C" " 101_4_C" " 031_1_C" " 059_1_S"
[928] " 105_1_C" " 008_2_C" " 029_4_C" " 094_1_S" " 034_3_C" " 081_3_S" " 002_2_C" " 081_4_C" " 033_1_C"
[937] " 074_2_C" " 106_1_C" " 035_4_S" " 035_1_C" " 011_2_S" " 007_3_S" " 005_1_S" " 001_4_C" " 101_3_C"
[946] " 028_2_C" " 101_1_C" " 013_1_S" " 096_2_C" " 002_3_C" " 038_4_S" " 076_4_C" " 098_2_C" " 040_2_S"
[955] " 012_1_C" " 102_2_C" " 001_3_S" " 002_3_S" " 012_4_C" " 076_2_S" " 096_3_S" " 032_2_S" " 002_4_S"
[964] " 079_2_C" " 098_4_C" " 095_2_C" " 080_4_C" " 030_3_S" " 040_3_C" " 040_4_S" " 076_1_S" " 029_2_C"
[973] " 032_3_C" " 105_2_S" " 003_1_C" " 096_3_C" " 102_1_C" " 102_1_S" " 032_4_C" " 076_3_S" " 104_1_C"
[982] " 098_2_S" " 102_3_S" " 075_2_S" " 099_3_S" " 009_4_S" " 081_2_C" " 031_3_C" " 009_3_C" " 006_1_C"
[991] " 009_4_C" " 058_3_C" " 008_4_C" " 078_1_S" " 103_2_C" " 076_2_C" " 008_4_S" " 095_3_C" " 104_4_C"
[1000] " 096_1_S"
[ reached getOption("max.print") -- omitted 76 entries ]all_MR2$genotype <- gsub("2800", "280", all_MR2$genotype)
all_MR2$root_name <- gsub(" 089_1_", " 089_1_C", all_MR2$root_name)
all_MR2$root_name <- gsub(" 089_1_C_C", " 089_1_C", all_MR2$root_name)
all_MR2$root_name <- gsub(" 089_1_C_S", " 089_1_S", all_MR2$root_name)
all_MR2$root_name <- gsub("301_3", "301_3_C", all_MR2$root_name)
all_MR2$root_name <- gsub("301_3_CC", "301_3_C", all_MR2$root_name)
all_MR2$root_name <- gsub("301_3_CS", "301_3_S", all_MR2$root_name)
unique(all_MR2$cond)[1] "C" "S" NA "s"all_MR2$cond <- gsub("s", "S", all_MR2$cond)
unique(all_MR2$cond)[1] "C" "S" NA normal_cond <- c("C", "S")
thats_it <- subset(all_MR2, (all_MR2$cond %in% normal_cond))
whats_that <- subset(all_MR2, !(all_MR2$cond %in% normal_cond))
whats_thatwhats_that$cond <- "C"
all_MR3 <- rbind(thats_it, whats_that)
unique(all_MR3$cond)[1] "C" "S"Histograms
Library loading
OK - now we can start plotting - finally! loading the libraries
library(ggplot2)
library(ggpubr)package ‘ggpubr’ was built under R version 4.0.2Registered S3 method overwritten by 'data.table':
method from
print.data.table library(doBy)
Attaching package: ‘doBy’
The following object is masked from ‘package:dplyr’:
order_bylibrary(cowplot)
********************************************************
Note: As of version 1.0.0, cowplot does not change the
default ggplot2 theme anymore. To recover the previous
behavior, execute:
theme_set(theme_cowplot())
********************************************************
Attaching package: ‘cowplot’
The following object is masked from ‘package:ggpubr’:
get_legendlibrary(RColorBrewer)
library(corrplot)corrplot 0.84 loadedlibrary(ggbeeswarm)
library(RColorBrewer)
library(ggridges)
library(gapminder)
library("gplots")package ‘gplots’ was built under R version 4.0.2
Attaching package: ‘gplots’
The following object is masked from ‘package:stats’:
lowesslibrary("colorRamps")Let’s start from a simple histogram - so we can see the treatment dependent change in TRS
TRS_histo <- gghistogram(all_MR3, x = "TRS",
add = "mean", rug = TRUE,
color = "cond", fill = "cond", facet.by = "DAS",
palette = c("#00AFBB", "#E7B800")) + xlab("Total root length (cm)")Using `bins = 30` by default. Pick better value with the argument `bins`.TRS_histo
OK - looks as we expect it to. Let’s have a look at other things - like LR number:
LRno_histo <- gghistogram(all_MR3, x = "LRno",
add = "mean", rug = TRUE,
color = "cond", fill = "cond", facet.by = "DAS",
palette = c("#00AFBB", "#E7B800")) + xlab("number of LR / Main Root")Using `bins = 30` by default. Pick better value with the argument `bins`.LRno_histo
LRL_histo <- gghistogram(all_MR3, x = "LRL",
add = "mean", rug = TRUE,
color = "cond", fill = "cond", facet.by = "DAS",
palette = c("#00AFBB", "#E7B800")) + xlab("Lateral Root Length (cm)")Using `bins = 30` by default. Pick better value with the argument `bins`.LRL_histo
MRL_histo <- gghistogram(all_MR3, x = "MRL",
add = "mean", rug = TRUE,
color = "cond", fill = "cond", facet.by = "DAS",
palette = c("#00AFBB", "#E7B800")) + xlab("Main root length (cm)")Using `bins = 30` by default. Pick better value with the argument `bins`.MRL_histo
CoG_histo <- gghistogram(all_MR3, x = "CoG",
add = "mean", rug = TRUE,
color = "cond", fill = "cond", facet.by = "DAS",
palette = c("#00AFBB", "#E7B800")) + xlab("Center of Gravity (cm of MR)")Using `bins = 30` by default. Pick better value with the argument `bins`.CoG_histo
all_MR3$LRL.dec <- as.numeric(all_MR3$LRL.dec)NAs introduced by coercionLRL.dec_histo <- gghistogram(all_MR3, x = "LRL.dec",
add = "mean", rug = TRUE,
color = "cond", fill = "cond", facet.by = "DAS",
palette = c("#00AFBB", "#E7B800")) + xlab("Lateral Root Length decrease over MR (cm / cm of MR)")Using `bins = 30` by default. Pick better value with the argument `bins`.LRL.dec_histo
the above doesnt really make sense - let’s replace all these extreme values (< -2, > 2) with “n.a.”)
temp <- subset(all_MR3, all_MR3$LRL.dec < 2)
temp <- subset(temp, temp$LRL.dec > -2)
LRL.dec_histo <- gghistogram(temp, x = "LRL.dec",
add = "mean", rug = TRUE,
color = "cond", fill = "cond", facet.by = "DAS",
palette = c("#00AFBB", "#E7B800")) + xlab("Lateral Root Length decrease over MR (cm / cm of MR)")Using `bins = 30` by default. Pick better value with the argument `bins`.LRL.dec_histo
all_MR3$LRL.dec.R2 <- as.numeric(all_MR3$LRL.dec.R2)NAs introduced by coercionLRL.dec.R2_histo <- gghistogram(all_MR3, x = "LRL.dec.R2",
add = "mean", rug = TRUE,
color = "cond", fill = "cond", facet.by = "DAS",
palette = c("#00AFBB", "#E7B800")) + xlab("R2 of Lateral Root Length decrease over MR")Using `bins = 30` by default. Pick better value with the argument `bins`.LRL.dec.R2_histo
visualization of changes in RSA across time:
Let’s try to visualize changes across the accessions throughout the time using the line graph - similar to ones in Awlia et al., 2020
First - what we need to do is to calculate the average per line and per condition:
colnames(all_MR3) [1] "image" "root_name" "root" "experiment" "DAS" "genotype" "rep"
[8] "cond" "MRL" "Apical" "Branched" "Basal" "Apical_perc" "Branched_perc"
[15] "Basal_perc" "LR_no_10_100" "LR_no_20_100" "LR_no_30_100" "LR_no_40_100" "LR_no_50_100" "LR_no_60_100"
[22] "LR_no_70_100" "LR_no_80_100" "LR_no_90_100" "LR_no_100_100" "LRL_10_100" "LRL_20_100" "LRL_30_100"
[29] "LRL_40_100" "LRL_50_100" "LRL_60_100" "LRL_70_100" "LRL_80_100" "LRL_90_100" "LRL_100_100"
[36] "CoG" "LRL.dec" "LRL.dec.R2" "LRL" "LRno" "aLRL" "TRS"
[43] "MRL.p.TRS" "aLRL.p.TRS" "LRL_10_perc" "LRL_20_perc" "LRL_30_perc" "LRL_40_perc" "LRL_50_perc"
[50] "LRL_60_perc" "LRL_70_perc" "LRL_80_perc" "LRL_90_perc" "LRL_100_perc" "LRno_10_perc" "LRno_20_perc"
[57] "LRno_30_perc" "LRno_40_perc" "LRno_50_perc" "LRno_60_perc" "LRno_70_perc" "LRno_80_perc" "LRno_90_perc"
[64] "LRno_100_perc"library(doBy)
MR_sum <- summaryBy(data = all_MR3, MRL + LRL + LRno + aLRL + TRS + MRL.p.TRS + aLRL.p.TRS + CoG + LRL.dec ~ genotype + cond + DAS)
head(MR_sum)MR_sum$geno_cond <- paste(MR_sum$genotype, "_", MR_sum$cond, sep="")TRS_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=TRS.mean, group = geno_cond, color = cond))
TRS_lgraph <- TRS_lgraph +geom_line(alpha = 0.1)
TRS_lgraph <- TRS_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)+ stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.TRS_lgraph <- TRS_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
TRS_lgraph <- TRS_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
TRS_lgraph <- TRS_lgraph + ylab("Total Root Size (cm)") + xlab("Days After Stress") + theme(legend.position='none')
TRS_lgraph
MRL_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=MRL.mean, group = geno_cond, color = cond))
MRL_lgraph <- MRL_lgraph +geom_line(alpha = 0.1)
MRL_lgraph <- MRL_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.MRL_lgraph <- MRL_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
MRL_lgraph <- MRL_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
MRL_lgraph <- MRL_lgraph + ylab("Main Root Length (cm)") + xlab("Days After Stress") + theme(legend.position='none')
MRL_lgraph
LRL_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=LRL.mean, group = geno_cond, color = cond))
LRL_lgraph <- LRL_lgraph +geom_line(alpha = 0.1)
LRL_lgraph <- LRL_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)+ stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRL_lgraph <- LRL_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_lgraph <- LRL_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
LRL_lgraph <- LRL_lgraph + ylab("Lateral Root Length (cm)") + xlab("Days After Stress") + theme(legend.position='none')
LRL_lgraph
LRno_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=LRno.mean, group = geno_cond, color = cond))
LRno_lgraph <- LRno_lgraph +geom_line(alpha = 0.1)
LRno_lgraph <- LRno_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRno_lgraph <- LRno_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_lgraph <- LRno_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
LRno_lgraph <- LRno_lgraph + ylab("Lateral Root Number (per MR)") + xlab("Days After Stress") + theme(legend.position='none')
LRno_lgraph
CoG_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=CoG.mean, group = geno_cond, color = cond))
CoG_lgraph <- CoG_lgraph +geom_line(alpha = 0.1)
CoG_lgraph <- CoG_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.CoG_lgraph <- CoG_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
CoG_lgraph <- CoG_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
CoG_lgraph <- CoG_lgraph + ylab("Center of Gravity (cm of MR)") + xlab("Days After Stress") + theme(legend.position='none')
CoG_lgraph
LRL.dec_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=LRL.dec.mean, group = geno_cond, color = cond))
LRL.dec_lgraph <- LRL.dec_lgraph +geom_line(alpha = 0.1)
LRL.dec_lgraph <- LRL.dec_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRL.dec_lgraph <- LRL.dec_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL.dec_lgraph <- LRL.dec_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
LRL.dec_lgraph <- LRL.dec_lgraph + ylab("decrease of LRL over MRL (cm / cm of MR)") + xlab("Days After Stress") + theme(legend.position='none')
LRL.dec_lgraph
MRL.p.TRS_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=MRL.p.TRS.mean, group = geno_cond, color = cond))
MRL.p.TRS_lgraph <- MRL.p.TRS_lgraph +geom_line(alpha = 0.1)
MRL.p.TRS_lgraph <- MRL.p.TRS_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.MRL.p.TRS_lgraph <- MRL.p.TRS_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
MRL.p.TRS_lgraph <- MRL.p.TRS_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
MRL.p.TRS_lgraph <- MRL.p.TRS_lgraph + ylab("ratio (MRL / TRS)") + xlab("Days After Stress") + theme(legend.position='none')
MRL.p.TRS_lgraph
aLRL.p.TRS_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=aLRL.p.TRS.mean, group = geno_cond, color = cond))
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph +geom_line(alpha = 0.1)
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph + ylab("ratio (aLRL / TRS)") + xlab("Days After Stress") + theme(legend.position='none')
aLRL.p.TRS_lgraph
Lets combine these into one figure:
pdf("Fig.TRS_MRL_LRL_LRno_CoG_MRLpTRS_Big01_to_Big04.pdf", height = 20, width = 15)
plot_grid(TRS_lgraph, MRL_lgraph,
LRL_lgraph, LRno_lgraph,
CoG_lgraph, MRL.p.TRS_lgraph, ncol=2, labels = "AUTO")
dev.off()null device
1 OK - let’s keep going with exploring other cool traits…
Vizualization of LRL/LRno distribution across the root:
In order to examine how the LR is distributed across MR - lets extract all the LR distribution traits
colnames(all_MR3) [1] "image" "root_name" "root" "experiment" "DAS" "genotype" "rep"
[8] "cond" "MRL" "Apical" "Branched" "Basal" "Apical_perc" "Branched_perc"
[15] "Basal_perc" "LR_no_10_100" "LR_no_20_100" "LR_no_30_100" "LR_no_40_100" "LR_no_50_100" "LR_no_60_100"
[22] "LR_no_70_100" "LR_no_80_100" "LR_no_90_100" "LR_no_100_100" "LRL_10_100" "LRL_20_100" "LRL_30_100"
[29] "LRL_40_100" "LRL_50_100" "LRL_60_100" "LRL_70_100" "LRL_80_100" "LRL_90_100" "LRL_100_100"
[36] "CoG" "LRL.dec" "LRL.dec.R2" "LRL" "LRno" "aLRL" "TRS"
[43] "MRL.p.TRS" "aLRL.p.TRS" "LRL_10_perc" "LRL_20_perc" "LRL_30_perc" "LRL_40_perc" "LRL_50_perc"
[50] "LRL_60_perc" "LRL_70_perc" "LRL_80_perc" "LRL_90_perc" "LRL_100_perc" "LRno_10_perc" "LRno_20_perc"
[57] "LRno_30_perc" "LRno_40_perc" "LRno_50_perc" "LRno_60_perc" "LRno_70_perc" "LRno_80_perc" "LRno_90_perc"
[64] "LRno_100_perc"dim(all_MR3)[1] 5511 64LR_distribution <- all_MR3[,c(1:8, 16:64)]
colnames(LR_distribution) [1] "image" "root_name" "root" "experiment" "DAS" "genotype" "rep"
[8] "cond" "LR_no_10_100" "LR_no_20_100" "LR_no_30_100" "LR_no_40_100" "LR_no_50_100" "LR_no_60_100"
[15] "LR_no_70_100" "LR_no_80_100" "LR_no_90_100" "LR_no_100_100" "LRL_10_100" "LRL_20_100" "LRL_30_100"
[22] "LRL_40_100" "LRL_50_100" "LRL_60_100" "LRL_70_100" "LRL_80_100" "LRL_90_100" "LRL_100_100"
[29] "CoG" "LRL.dec" "LRL.dec.R2" "LRL" "LRno" "aLRL" "TRS"
[36] "MRL.p.TRS" "aLRL.p.TRS" "LRL_10_perc" "LRL_20_perc" "LRL_30_perc" "LRL_40_perc" "LRL_50_perc"
[43] "LRL_60_perc" "LRL_70_perc" "LRL_80_perc" "LRL_90_perc" "LRL_100_perc" "LRno_10_perc" "LRno_20_perc"
[50] "LRno_30_perc" "LRno_40_perc" "LRno_50_perc" "LRno_60_perc" "LRno_70_perc" "LRno_80_perc" "LRno_90_perc"
[57] "LRno_100_perc"Cool - then let’s extract only the last day, reshape the data and plot the absolute LRno and LRL across the MR portions:
4 days after stress
day4 <- subset(LR_distribution, LR_distribution$DAS == 4)
head(day4)colnames(day4) [1] "image" "root_name" "root" "experiment" "DAS" "genotype" "rep"
[8] "cond" "LR_no_10_100" "LR_no_20_100" "LR_no_30_100" "LR_no_40_100" "LR_no_50_100" "LR_no_60_100"
[15] "LR_no_70_100" "LR_no_80_100" "LR_no_90_100" "LR_no_100_100" "LRL_10_100" "LRL_20_100" "LRL_30_100"
[22] "LRL_40_100" "LRL_50_100" "LRL_60_100" "LRL_70_100" "LRL_80_100" "LRL_90_100" "LRL_100_100"
[29] "CoG" "LRL.dec" "LRL.dec.R2" "LRL" "LRno" "aLRL" "TRS"
[36] "MRL.p.TRS" "aLRL.p.TRS" "LRL_10_perc" "LRL_20_perc" "LRL_30_perc" "LRL_40_perc" "LRL_50_perc"
[43] "LRL_60_perc" "LRL_70_perc" "LRL_80_perc" "LRL_90_perc" "LRL_100_perc" "LRno_10_perc" "LRno_20_perc"
[50] "LRno_30_perc" "LRno_40_perc" "LRno_50_perc" "LRno_60_perc" "LRno_70_perc" "LRno_80_perc" "LRno_90_perc"
[57] "LRno_100_perc"LRno <- day4[,c(1:18)]
head(LRno)LRL <- day4[,c(1:8,19:28)]
library(reshape2)
Attaching package: ‘reshape2’
The following object is masked from ‘package:tidyr’:
smithsmLRno <- melt(LRno, value.name = "LRno")Using image, root_name, root, experiment, DAS, genotype, rep, cond as id variablesmLRL <- melt(LRL, value.name = "LRL")Using image, root_name, root, experiment, DAS, genotype, rep, cond as id variableshead(mLRno)head(mLRL)cool - now let’s adjust the portion of the MR that is being used to calculate the LR distribution in “variable” columns:
mLRno$variable <- gsub("LR_no_", "", mLRno$variable)
mLRno$variable <- gsub("_100", "", mLRno$variable)
mLRL$variable <- gsub("LRL_", "", mLRL$variable)
mLRL$variable <- gsub("_100", "", mLRL$variable)
mLRL$variable <- as.numeric(mLRL$variable)
mLRno$variable <- as.numeric(mLRno$variable)
colnames(mLRL)[9] <- "perc.of.MRL"
colnames(mLRno)[9] <- "perc.of.MRL"
head(mLRL)head(mLRno)NALRL_distribution_graph_d4 <- ggplot(data=mLRL, aes(x= perc.of.MRL, y=LRL, group = root_name, color = cond))
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1"))
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("4 Days After Stress")
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + theme(legend.position="none")+ labs(color = "Treatment")
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + ylab("Lateral Root Length (cm)") + xlab("Portion of Main Root Length (%)")
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_distribution_graph_d4
LRno_distribution_graph_d4 <- ggplot(data=mLRno, aes(x= perc.of.MRL, y=LRno, group = root_name, color = cond))
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1"))
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("4 Days After Stress")
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + theme(legend.position="none")+ labs(color = "Treatment")
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + ylab("Lateral Root number") + xlab("Portion of Main Root Length (%)")
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_distribution_graph_d4
all good - but I think it would be better to calculate the above as %of LRL or %LRno per plant - then we can get better idea for clustering:
colnames(day4) [1] "image" "root_name" "root" "experiment" "DAS" "genotype" "rep"
[8] "cond" "LR_no_10_100" "LR_no_20_100" "LR_no_30_100" "LR_no_40_100" "LR_no_50_100" "LR_no_60_100"
[15] "LR_no_70_100" "LR_no_80_100" "LR_no_90_100" "LR_no_100_100" "LRL_10_100" "LRL_20_100" "LRL_30_100"
[22] "LRL_40_100" "LRL_50_100" "LRL_60_100" "LRL_70_100" "LRL_80_100" "LRL_90_100" "LRL_100_100"
[29] "CoG" "LRL.dec" "LRL.dec.R2" "LRL" "LRno" "aLRL" "TRS"
[36] "MRL.p.TRS" "aLRL.p.TRS" "LRL_10_perc" "LRL_20_perc" "LRL_30_perc" "LRL_40_perc" "LRL_50_perc"
[43] "LRL_60_perc" "LRL_70_perc" "LRL_80_perc" "LRL_90_perc" "LRL_100_perc" "LRno_10_perc" "LRno_20_perc"
[50] "LRno_30_perc" "LRno_40_perc" "LRno_50_perc" "LRno_60_perc" "LRno_70_perc" "LRno_80_perc" "LRno_90_perc"
[57] "LRno_100_perc"LRno_perc <- day4[,c(1:8,48:57)]
LRL_perc <- day4[,c(1:8,38:47)]
head(LRno_perc)head(LRL_perc)mLRno_perc <- melt(LRno_perc, value.name = "LRno.perc")Using image, root_name, root, experiment, DAS, genotype, rep, cond as id variablesmLRL_perc <- melt(LRL_perc, value.name = "LRL.perc")Using image, root_name, root, experiment, DAS, genotype, rep, cond as id variableshead(mLRno_perc)head(mLRL_perc)unique(mLRno_perc$variable) [1] LRno_10_perc LRno_20_perc LRno_30_perc LRno_40_perc LRno_50_perc LRno_60_perc LRno_70_perc LRno_80_perc
[9] LRno_90_perc LRno_100_perc
10 Levels: LRno_10_perc LRno_20_perc LRno_30_perc LRno_40_perc LRno_50_perc LRno_60_perc ... LRno_100_percmLRno_perc$variable <- gsub("LRno_", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("LRL_", "", mLRL_perc$variable)
mLRno_perc$variable <- gsub("_perc", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("_perc", "", mLRL_perc$variable)
mLRno_perc$variable <- as.numeric(mLRno_perc$variable)
mLRL_perc$variable <- as.numeric(mLRL_perc$variable)
colnames(mLRno_perc)[9] <- "perc.of.MRL"
colnames(mLRL_perc)[9] <- "perc.of.MRL"
head(mLRno_perc)head(mLRL_perc)OK - now let’s recalculate these graphs:
LRL_distribution_graph_d4_perc <- ggplot(data=mLRL_perc, aes(x= perc.of.MRL, y=LRL.perc, group = root_name, color = cond))
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1"))
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("4 Days After Stress")
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + ylab("Lateral Root Length (fraction of total LRL)") + xlab("Portion of Main Root Length (%)")
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_distribution_graph_d4_perc
LRno_distribution_graph_d4_perc <- ggplot(data=mLRno_perc, aes(x= perc.of.MRL, y=LRno.perc, group = root_name, color = cond))
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1"))
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("4 Days After Stress")
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + ylab("Lateral Root number (fraction of total LR #)") + xlab("Portion of Main Root Length (%)")
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_distribution_graph_d4_perc
OK - so the percentage of LRL is definitely more informative. Let’s have a look how it looks in earlier days:
3 days after stress
day3 <- subset(all_MR3, all_MR3$DAS == 3)
LRno_perc <- day3[,c(1:8,48:57)]
LRL_perc <- day3[,c(1:8,38:47)]
mLRno_perc <- melt(LRno_perc, value.name = "LRno.perc")Using image, root_name, root, experiment, DAS, genotype, rep, cond as id variablesmLRL_perc <- melt(LRL_perc, value.name = "LRL.perc")Using image, root_name, root, experiment, DAS, genotype, rep, cond as id variablesmLRno_perc$variable <- gsub("LRno_", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("LRL_", "", mLRL_perc$variable)
mLRno_perc$variable <- gsub("_perc", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("_perc", "", mLRL_perc$variable)
mLRno_perc$variable <- as.numeric(mLRno_perc$variable)NAs introduced by coercionmLRL_perc$variable <- as.numeric(mLRL_perc$variable)NAs introduced by coercioncolnames(mLRno_perc)[9] <- "perc.of.MRL"
colnames(mLRL_perc)[9] <- "perc.of.MRL"
LRL_distribution_graph_d3_perc <- ggplot(data=mLRL_perc, aes(x= perc.of.MRL, y=LRL.perc, group = root_name, color = cond))
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("3 Days After Stress")
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + ylab("Lateral Root Length (fraction of total LRL)") + xlab("Portion of Main Root Length (%)")
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_distribution_graph_d3_perc
LRno_distribution_graph_d3_perc <- ggplot(data=mLRno_perc, aes(x= perc.of.MRL, y=LRno.perc, group = root_name, color = cond))
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("3 Days After Stress")
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + ylab("Lateral Root number (fraction of total LR #)") + xlab("Portion of Main Root Length (%)")
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_distribution_graph_d3_perc
day2 <- subset(all_MR3, all_MR3$DAS == 2)
LRno_perc <- day2[,c(1:8,48:57)]
LRL_perc <- day2[,c(1:8,38:47)]
mLRno_perc <- melt(LRno_perc, value.name = "LRno.perc")Using image, root_name, root, experiment, DAS, genotype, rep, cond as id variablesmLRL_perc <- melt(LRL_perc, value.name = "LRL.perc")Using image, root_name, root, experiment, DAS, genotype, rep, cond as id variablesmLRno_perc$variable <- gsub("LRno_", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("LRL_", "", mLRL_perc$variable)
mLRno_perc$variable <- gsub("_perc", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("_perc", "", mLRL_perc$variable)
mLRno_perc$variable <- as.numeric(mLRno_perc$variable)NAs introduced by coercionmLRL_perc$variable <- as.numeric(mLRL_perc$variable)NAs introduced by coercioncolnames(mLRno_perc)[9] <- "perc.of.MRL"
colnames(mLRL_perc)[9] <- "perc.of.MRL"
LRL_distribution_graph_d2_perc <- ggplot(data=mLRL_perc, aes(x= perc.of.MRL, y=LRL.perc, group = root_name, color = cond))
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("2 Days After Stress")
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + ylab("Lateral Root Length (fraction of total LRL)") + xlab("Portion of Main Root Length (%)")
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_distribution_graph_d2_perc
LRno_distribution_graph_d2_perc <- ggplot(data=mLRno_perc, aes(x= perc.of.MRL, y=LRno.perc, group = root_name, color = cond))
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("2 Days After Stress")
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + ylab("Lateral Root number (fraction of total LR #)") + xlab("Portion of Main Root Length (%)")
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_distribution_graph_d2_perc
day1 <- subset(all_MR3, all_MR3$DAS == 1)
LRno_perc <- day1[,c(1:8,48:57)]
LRL_perc <- day1[,c(1:8,38:47)]
mLRno_perc <- melt(LRno_perc, value.name = "LRno.perc")Using image, root_name, root, experiment, DAS, genotype, rep, cond as id variablesmLRL_perc <- melt(LRL_perc, value.name = "LRL.perc")Using image, root_name, root, experiment, DAS, genotype, rep, cond as id variablesmLRno_perc$variable <- gsub("LRno_", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("LRL_", "", mLRL_perc$variable)
mLRno_perc$variable <- gsub("_perc", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("_perc", "", mLRL_perc$variable)
mLRno_perc$variable <- as.numeric(mLRno_perc$variable)NAs introduced by coercionmLRL_perc$variable <- as.numeric(mLRL_perc$variable)NAs introduced by coercioncolnames(mLRno_perc)[9] <- "perc.of.MRL"
colnames(mLRL_perc)[9] <- "perc.of.MRL"
LRL_distribution_graph_d1_perc <- ggplot(data=mLRL_perc, aes(x= perc.of.MRL, y=LRL.perc, group = root_name, color = cond))
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("1 Day After Stress")
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + ylab("Lateral Root Length (fraction of total LRL)") + xlab("Portion of Main Root Length (%)")
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_distribution_graph_d1_perc
LRno_distribution_graph_d1_perc <- ggplot(data=mLRno_perc, aes(x= perc.of.MRL, y=LRno.perc, group = root_name, color = cond))
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("1 Day After Stress")
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + ylab("Lateral Root number (fraction of total LR #)") + xlab("Portion of Main Root Length (%)")
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_distribution_graph_d1_perc
ok - maybe let’s combine these graphs into a figure thus far:
library(cowplot)
pdf("Fig.Lateral_root_distribution_across_MRL_DAS1_DAS4_Big01_to_Big04.pdf", height = 20, width = 15)
plot_grid(LRL_distribution_graph_d1_perc, LRno_distribution_graph_d1_perc,
LRL_distribution_graph_d2_perc, LRno_distribution_graph_d2_perc,
LRL_distribution_graph_d3_perc, LRno_distribution_graph_d3_perc,
LRL_distribution_graph_d4_perc, LRno_distribution_graph_d4_perc, ncol=2, labels = "AUTO")Removed 8409 rows containing non-finite values (stat_summary).Removed 8409 rows containing non-finite values (stat_summary).Removed 8409 rows containing non-finite values (stat_compare_means).Removed 8381 row(s) containing missing values (geom_path).Removed 8409 rows containing non-finite values (stat_summary).Removed 8409 rows containing non-finite values (stat_summary).Removed 8409 rows containing non-finite values (stat_compare_means).Removed 8381 row(s) containing missing values (geom_path).Removed 8072 rows containing non-finite values (stat_summary).Removed 8072 rows containing non-finite values (stat_summary).Removed 8072 rows containing non-finite values (stat_compare_means).Removed 8056 row(s) containing missing values (geom_path).Removed 8072 rows containing non-finite values (stat_summary).Removed 8072 rows containing non-finite values (stat_summary).Removed 8072 rows containing non-finite values (stat_compare_means).Removed 8056 row(s) containing missing values (geom_path).Removed 7825 rows containing non-finite values (stat_summary).Removed 7825 rows containing non-finite values (stat_summary).Removed 7825 rows containing non-finite values (stat_compare_means).Removed 7823 row(s) containing missing values (geom_path).Removed 7825 rows containing non-finite values (stat_summary).Removed 7825 rows containing non-finite values (stat_summary).Removed 7825 rows containing non-finite values (stat_compare_means).Removed 7823 row(s) containing missing values (geom_path).Removed 120 rows containing non-finite values (stat_summary).Removed 120 rows containing non-finite values (stat_summary).Removed 120 rows containing non-finite values (stat_compare_means).Removed 120 row(s) containing missing values (geom_path).Removed 120 rows containing non-finite values (stat_summary).Removed 120 rows containing non-finite values (stat_summary).Removed 120 rows containing non-finite values (stat_compare_means).Removed 120 row(s) containing missing values (geom_path).dev.off()null device
1 Calculating growth rates for each plant:
OK - so now we have all the data organized and everything seems to look pretty sane - let’s start calculating the growth rate of aLRL, LR# and MR. The problem is that in some cases - the MR grew into the plate - and we didnt continue tracing it - so the length is getting staled. So - in these cases - I would like to calculate the growth rate only over the period where the size of the root is actually increasing. So:
First - set the “day” ad numeric variable - because this will later come and bite us in our asses:
all_MR3$DAS <- as.numeric(as.character(all_MR3$DAS))
head(all_MR3)Establishing calculations
Second - isolate one plant from the dataset, sort per day, and mark which of the values for MR are duplicates:
temp1 <- subset(all_MR3, all_MR3$root == unique(all_MR3$root)[100])
temp2 <- temp1[order(temp1$DAS),]
head(temp2)
temp2$MRdouble <- "no"
for(i in 2:5){
if(temp2$MRL[i] == temp2$MRL[i-1]){
temp2$MRdouble[i] <- "yes"
}
else{
temp2$MRdouble[i] <- "no"
}
}
temp3 <- subset(temp2, temp2$MRdouble == "no")
temp3Now that we have the dataset without duplicated values - let’s calculate MR growth rate using linear function:
plot(temp3$MRL ~ temp3$DAS)
abline(lm(temp3$MRL ~ temp3$DAS))
Let’s check if this is also true for #LR:
temp2$LRnodouble <- "no"
for(i in 2:5){
if(temp2$LRno[i] == temp2$LRno[i-1]){
temp2$LRnodouble[i] <- "yes"
}
else{
temp2$LRnodouble[i] <- "no"
}
}
temp3 <- subset(temp2, temp2$LRnodouble == "no")
plot(temp3$LRno ~ temp3$DAS)
abline(lm(temp3$LRno ~ temp3$DAS))
and for aLRL:
temp2$aLRLdouble <- "no"
for(i in 2:5){
if(temp2$aLRL[i] == temp2$aLRL[i-1]){
temp2$aLRLdouble[i] <- "yes"
}
else{
temp2$aLRLdouble[i] <- "no"
}
}
temp2
temp3 <- subset(temp2, temp2$aLRLdouble == "no")
plot(temp3$aLRL ~ temp3$DAS)
abline(lm(temp3$aLRL ~ temp3$DAS))
Looped linear modeling for all RSA components
First - let’s construct the dataframe which will hold all the data points:
names <- c(text="root_name", "genotype", "cond", "MR.intercept", "MR.delta", "LRno.intercept", "LRno.delta", "aLRL.intercept", "aLRL.delta")
growth_factors <- data.frame()
for (k in names) growth_factors[[k]] <- as.character()
just few fixes:
all_MR3$root_name<- gsub("2800_3_S", "280_3_S", all_MR3$root_name)
all_MR3 <- subset(all_MR3, all_MR3$image != "_set1_day1_20190930_093.rsml")
all_MR3 <- subset(all_MR3, all_MR3$image != "_set1_day1_20190930_090.rsml")
all_MR3 <- subset(all_MR3, all_MR3$image != "_set1_day1_20190930_067.rsml")
all_MR3 <- subset(all_MR3, all_MR3$image != "_set1_day1_20191008_047_FU.rsml")
Then - let’s loop it in for calculating the Main Root growth factors:
for(e in c(1:length(unique(all_MR3$root_name)))){
temp1 <- subset(all_MR3, all_MR3$root_name == unique(all_MR3$root_name)[e])
temp2 <- temp1[order(temp1$DAS),]
dim(temp2)
if(dim(temp2)[1] < 2){
}
# Main root part
temp2$MRdouble <- "no"
for(i in 2:nrow(temp2)){
if(temp2$MRL[i] == temp2$MRL[i-1]){
temp2$MRdouble[i] <- "yes"
}
else{
temp2$MRdouble[i] <- "no"
}
}
temp2
# Main Root Growth Factor calculations:
temp3 <- subset(temp2, temp2$MRdouble == "no")
model <- lm(temp3$MRL ~ temp3$DAS)
growth_factors[e,1] <- temp3$root_name[1]
growth_factors[e,2] <- temp3$genotype[1]
growth_factors[e,3] <- temp3$cond[1]
growth_factors[e,4] <- as.numeric(as.character(model$coefficients[[1]]))
growth_factors[e,5] <- as.numeric(as.character(model$coefficients[[2]]))
}dim(growth_factors)[1] 1072 9Great - let’s calculate the LRno increase rate - with some special conditions for the lines which don’t have any LR:
for(e in 1:length(unique(all_MR3$root_name))){
temp1 <- subset(all_MR3, all_MR3$root_name == unique(all_MR3$root_name)[e])
temp2 <- temp1[order(temp1$DAS),]
# Let's remove all NA for LRno and aLRL in temp2
super_temp <- temp2[,c("DAS", "LRno", "aLRL")]
temp2 <- na.omit(super_temp)
if(dim(temp2)[1] > 1){
model <- lm(temp2$LRno ~ temp2$DAS)
growth_factors[e,6] <- as.numeric(as.character(model$coefficients[[1]]))
growth_factors[e,7] <- as.numeric(as.character(model$coefficients[[2]]))
}
# Lateral Root Number Increase calculations:
else{
growth_factors[e,6] <- 0
growth_factors[e,7] <- 0
}
} Same thing for aLRL:
for(e in 1:length(unique(all_MR3$root_name))){
temp1 <- subset(all_MR3, all_MR3$root_name == unique(all_MR3$root_name)[e])
temp2 <- temp1[order(temp1$DAS),]
# Let's remove all NA for LRno and aLRL in temp2
super_temp <- temp2[,c("DAS", "LRno", "aLRL")]
temp2 <- na.omit(super_temp)
temp2
dim(temp2)[1]
if(dim(temp2)[1] > 1){
model <- lm(temp2$aLRL ~ temp2$DAS)
model
growth_factors[e,8] <- as.numeric(as.character(model$coefficients[[1]]))
growth_factors[e,9] <- as.numeric(as.character(model$coefficients[[2]]))
}
# Lateral Root Number Increase calculations:
else{
growth_factors[e,8] <- 0
growth_factors[e,9] <- 0
}
} Let’s have a look at the growth_factors table:
growth_factorsmin(growth_factors$MR.delta)[1] "-0.1994374"min(growth_factors$LRno.delta)[1] NAmin(growth_factors$aLRL.delta)[1] NAmax(growth_factors$MR.delta)[1] "3.24117685"max(growth_factors$LRno.delta)[1] NAmax(growth_factors$aLRL.delta)[1] NAsapply(growth_factors, class) root_name genotype cond MR.intercept MR.delta LRno.intercept LRno.delta
"character" "character" "character" "character" "character" "character" "character"
aLRL.intercept aLRL.delta
"character" "character" growth_factors$MR.delta <- as.numeric(growth_factors$MR.delta)
growth_factors$LRno.delta <- as.numeric(growth_factors$LRno.delta)
growth_factors$aLRL.delta <- as.numeric(growth_factors$aLRL.delta)Histograms for growth rates
Most values seem to be in the reasonable range - but let’s have a look at the histogram
MR.delta_histo <- gghistogram(growth_factors, x = "MR.delta",
add = "mean", rug = TRUE,
color = "cond", fill = "cond",
palette = c("#00AFBB", "#E7B800")) + xlab("Main Root Growth (cm / day)")Using `bins = 30` by default. Pick better value with the argument `bins`.MR.delta_histo
LRno.delta_histo <- gghistogram(growth_factors, x = "LRno.delta",
add = "mean", rug = TRUE,
color = "cond", fill = "cond",
palette = c("#00AFBB", "#E7B800")) + xlab("Increase in Lateral Root number (# LR / day)")Using `bins = 30` by default. Pick better value with the argument `bins`.LRno.delta_histo
aLRL.delta_histo <- gghistogram(growth_factors, x = "aLRL.delta",
add = "mean", rug = TRUE,
color = "cond", fill = "cond",
palette = c("#00AFBB", "#E7B800")) + xlab("average Lateral Root Growth (cm / day)")Using `bins = 30` by default. Pick better value with the argument `bins`.aLRL.delta_histo
awesome - let’s save this into one figure as well:
pdf("Fig.Growth_rates_histograms_MR_noLR_aLRL.pdf", width = 7.5, height = 10)
plot_grid(MR.delta_histo, LRno.delta_histo, aLRL.delta_histo, ncol=1, labels = "AUTO")Removed 1 rows containing non-finite values (stat_bin).Removed 1 rows containing non-finite values (stat_bin).dev.off()null device
1 reproducibility within accessions for growth rates:
let’s have a look what is the diversity of growth rates within accessions:
grand.means <- aggregate(MR.delta ~ cond, data = growth_factors, FUN = mean)
grand.means
unique(growth_factors$genotype) [1] " 189" " 292" " 213" " 212" " 307" " 282" " 298" " 301" " 286" " 214" " 194" " 294" " 285" " 293" " 284" " 278"
[17] " 300" " 201" " 279" " 280" " 299" " 200" " 211" " 308" " 197" " 287" " 196" " 220" " 207" " 290" " 198" " 195"
[33] " 289" " 288" " 291" " 199" " 277" " 283" " 306" " 235" " 192" " 147" " 252" " 166" " 269" " 064" " 253" " 193"
[49] " 146" " 141" " 251" " 139" " 169" " 258" " 144" " 138" " 061" " 065" " 170" " 142" " 140" " 168" " 272" " 165"
[65] " 255" " 260" " 264" " 262" " 261" " 167" " 060" " 066" " 136" " 256" " 143" " 171" " 082" " 083" " 191" " 259"
[81] " 062" " 172" " 063" " 270" " 268" " 267" " 234" " 245" " 173" " 236" " 084" " 229" " 241" " 231" " 227" " 176"
[97] " 087" " 091" " 090" " 085" " 228" " 230" " 175" " 250" " 222" " 239" " 233" " 274" " 088" " 271" " 240" " 092"
[113] " 089" " 247" " 038" " 248" " 086" " 273" " 244" " 093" " 174" " 249" " 237" " 221" " 225" " 103" " 028" " 098"
[129] " 055" " 075" " 059" " 101" " 034" " 002" " 030" " 056" " 001" " 102" " 012" " 076" " 079" " 104" " 081" " 096"
[145] " 058" " 099" " 105" " 040" " 035" " 094" " 074" " 032" " 022" " 003" " 095" " 009" " 106" " 057" " 033" " 029"
[161] " 013" " 031" " 007" " 008" " 006" " 080" " 005" " 036" " 011" " 078" " 010" " 097" " 037"MR.delta_p_geno <- ggerrorplot(growth_factors, y = "MR.delta", x = "genotype", fill="genotype", color="genotype",
facet.by = c("cond"), ncol=1,
desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"),
xlab="Genotype", ylab="Main Root Growth (cm / day)")
MR.delta_p_geno <- MR.delta_p_geno + geom_hline(
data = grand.means, aes(yintercept = MR.delta),
linetype = 2,
group = "cond")
MR.delta_p_geno <- MR.delta_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = ".all.",
label = "p.signif", hide.ns = T)
MR.delta_p_geno
grand.means <- aggregate(LRno.delta ~ cond, data = growth_factors, FUN = mean)
grand.means
LRno.delta_p_geno <- ggerrorplot(growth_factors, y = "LRno.delta", x = "genotype", fill="genotype", color="genotype",
facet.by = c("cond"), ncol=1,
desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"),
xlab="Genotype", ylab="Increase in LR number (# LR / day)")
LRno.delta_p_geno <- LRno.delta_p_geno + geom_hline(
data = grand.means, aes(yintercept = LRno.delta),
linetype = 2,
group = "cond")
LRno.delta_p_geno <- LRno.delta_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = ".all.",
label = "p.signif", hide.ns = T)
LRno.delta_p_geno
grand.means <- aggregate(aLRL.delta ~ cond, data = growth_factors, FUN = mean)
grand.means
aLRL.delta_p_geno <- ggerrorplot(growth_factors, y = "aLRL.delta", x = "genotype", fill="genotype", color="genotype",
facet.by = c("cond"), ncol=1,
desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"),
xlab="Genotype", ylab="average Lateral Root Growth (cm / day)")
aLRL.delta_p_geno <- aLRL.delta_p_geno + geom_hline(
data = grand.means, aes(yintercept = aLRL.delta),
linetype = 2,
group = "cond")
aLRL.delta_p_geno <- aLRL.delta_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = ".all.",
label = "p.signif", hide.ns = T)
aLRL.delta_p_geno
ok - but these trends are very general - let’s have a look what is the diversity between accessions in their salt responses.
stress-induced changes in growth rates:
Also - maybe lets calculate the average growth rate per accession and look how this looks on a graph with some stats - as in - do we see sig. differences between C and S:
head(growth_factors)growth_nona <- na.omit(growth_factors)
sum_growth <- summaryBy(data = growth_factors, MR.delta + LRno.delta + aLRL.delta ~ cond + genotype)
sum_growth
MR.delta_dotplot <- ggerrorplot(sum_growth, y = "MR.delta.mean", x = "cond", fill="cond", color="cond",
desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"),
xlab="Condition", ylab="Main Root Growth (cm / day)")
MR.delta_dotplot <- MR.delta_dotplot + rremove("legend") + stat_compare_means(method="t.test", ref.group = "C",
label = "p.signif", hide.ns = T)
MR.delta_dotplot
aLRL.delta_dotplot <- ggerrorplot(sum_growth, y = "aLRL.delta.mean", x = "cond", fill="cond", color="cond",
desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"),
xlab="Condition", ylab="average Lateral Root Growth (cm / day)")
aLRL.delta_dotplot <- aLRL.delta_dotplot + rremove("legend") + stat_compare_means(method="t.test", ref.group = "C",
label = "p.signif", hide.ns = T)
aLRL.delta_dotplot
LRno.delta_dotplot <- ggerrorplot(sum_growth, y = "LRno.delta.mean", x = "cond", fill="cond", color="cond",
desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"),
xlab="Condition", ylab="average Lateral Root Growth (cm / day)")
LRno.delta_dotplot <- LRno.delta_dotplot + rremove("legend") + stat_compare_means(method="t.test", ref.group = "C",
label = "p.signif", hide.ns = T)
LRno.delta_dotplot
ok - now we have all the accession values averaged - let’s compare the growth rate under control and salt stress
First - we need to split the data into control and salt and then match them based on genotype:
growth_C <- subset(sum_growth, sum_growth$cond == "C")
growth_S <- subset(sum_growth, sum_growth$cond == "S")
colnames(growth_C) <- gsub(".mean", ".Control", colnames(growth_C))
colnames(growth_S) <- gsub(".mean", ".Salt", colnames(growth_S))
growth_C2 <- growth_C[,c(2:5)]
growth_S2 <- growth_S[,c(2:5)]
sum_growth2 <- merge(growth_C2, growth_S2, by="genotype")
sum_growth2corrplot_MR_gr <- ggscatter(sum_growth2, x = "MR.delta.Control", y = "MR.delta.Salt", size = 0.3,
rug = TRUE, add = "reg.line", color = "LRno.delta.Control") + stat_cor(method = "pearson")
corrplot_MR_gr <- corrplot_MR_gr + gradient_color(c("blue", "grey", "red"))
corrplot_MR_gr
corrplot_aLRL_gr <- ggscatter(sum_growth2, x = "aLRL.delta.Control", y = "aLRL.delta.Salt", size = 0.3,
rug = TRUE, add = "reg.line", color = "aLRL.delta.Control") + stat_cor(method = "pearson")
corrplot_aLRL_gr <- corrplot_aLRL_gr + gradient_color(c("blue", "grey", "red"))
corrplot_aLRL_gr
NA
NAcorrplot_LRno_gr <- ggscatter(sum_growth2, x = "LRno.delta.Control", y = "LRno.delta.Salt", color = "LRno.delta.Control",
size = 0.3, rug = TRUE, add = "reg.line") + stat_cor(method = "pearson")
corrplot_LRno_gr <- corrplot_LRno_gr + gradient_color(c("blue", "grey", "red"))
corrplot_LRno_gr
hmmm… thinking about the dotplots from earlier and they should actually be a paired graph, because we want to see how individual accessions are behaving, and whether that behaviour is conserved across accessions:
MR_sum_growth <- sum_growth2[,c(1,2,5)]
MR_sum_melt <- melt(MR_sum_growth)Using genotype as id variablesMR_sum_meltMR_sum_melt$variable <- gsub("MR.delta.", "", MR_sum_melt$variable)
colnames(MR_sum_melt)[2] <- "condition"
colnames(MR_sum_melt)[3] <- "MR.delta"
MR_growth_paired <- ggpaired(MR_sum_melt, x = "condition", y = "MR.delta",
color = "condition", line.color = "gray", line.size = 0.1,
palette = "jco", xlab ="", ylab ="Main Root Growth (cm / day)")
MR_growth_paired <- MR_growth_paired + stat_compare_means(paired = TRUE, label.x = 1.5) + rremove("legend")
MR_growth_paired
Cool - that looks much more informative - let’s do this for other traits:
LRno_sum_growth <- sum_growth2[,c(1,3,6)]
LRno_sum_growth <- na.omit(LRno_sum_growth)
LRno_sum_melt <- melt(LRno_sum_growth)Using genotype as id variablesLRno_sum_meltLRno_sum_melt$variable <- gsub("LRno.delta.", "", LRno_sum_melt$variable)
colnames(LRno_sum_melt)[2] <- "condition"
colnames(LRno_sum_melt)[3] <- "LRno.delta"
LRno_growth_paired <- ggpaired(LRno_sum_melt, x = "condition", y = "LRno.delta",
color = "condition", line.color = "gray", line.size = 0.1,
palette = "jco", xlab ="", ylab ="Increase in Lateral Root # (# LR / day)")
LRno_growth_paired <- LRno_growth_paired + stat_compare_means(paired = TRUE, label.x = 1.5) + rremove("legend")
LRno_growth_paired
aLRL_sum_growth <- sum_growth2[,c(1,4,7)]
aLRL_sum_growth <- na.omit(aLRL_sum_growth)
aLRL_sum_melt <- melt(aLRL_sum_growth)Using genotype as id variablesaLRL_sum_meltaLRL_sum_melt$variable <- gsub("aLRL.delta.", "", aLRL_sum_melt$variable)
colnames(aLRL_sum_melt)[2] <- "condition"
colnames(aLRL_sum_melt)[3] <- "aLRL.delta"
aLRL_growth_paired <- ggpaired(aLRL_sum_melt, x = "condition", y = "aLRL.delta",
color = "condition", line.color = "gray", line.size = 0.1,
palette = "jco", xlab ="", ylab ="average Lateral Root Growth (cm / day)")
aLRL_growth_paired <- aLRL_growth_paired + stat_compare_means(paired = TRUE, label.x = 1.5) + rremove("legend")
aLRL_growth_paired
ok - now let’s start comparing how individual accessions are doing when it comes to salt indices of individual organs:
Salt Tolerance Indexes
In order to look at salt responses - we need to calculate the average plant responses per accession and calculate the relative change in growth rate in response to salt (relative to control):
head(sum_growth2)
sum_growth2$MR.STI <- sum_growth2$MR.delta.Salt/sum_growth2$MR.delta.Control
sum_growth2$LRno.STI <- sum_growth2$LRno.delta.Salt/sum_growth2$LRno.delta.Control
sum_growth2$aLRL.STI <- sum_growth2$aLRL.delta.Salt/sum_growth2$aLRL.delta.Control
head(sum_growth2)Then - we would like to know also what is the effect of the salt on different root components across different accessions - so let’s make a histogram for this :)
colnames(sum_growth2) [1] "genotype" "MR.delta.Control" "LRno.delta.Control" "aLRL.delta.Control" "MR.delta.Salt"
[6] "LRno.delta.Salt" "aLRL.delta.Salt" "MR.STI" "LRno.STI" "aLRL.STI" STI_histo_set <- sum_growth2[,c(1,8:10)]
head(STI_histo_set)STI_histo_melt <- melt(STI_histo_set)Using genotype as id variableshead(STI_histo_melt)
STI_growth_histo <- gghistogram(STI_histo_melt, x = "value",
add = "mean", rug = TRUE,
color = "variable", fill = "variable",
palette = c("#1b9e77", "#d95f02", "#7570b3")) + xlab("fraction of growth at control")Using `bins = 30` by default. Pick better value with the argument `bins`.STI_growth_histo
cool - now we can also look in detail of how the individual accessions behave throughout these three STI
STI_growth_paired <- ggpaired(STI_histo_melt, x = "variable", y = "value",
color = "variable", line.color = "gray", line.size = 0.1,
palette = "jco", xlab ="", ylab ="Fraction of rate at Control")
STI_growth_paired <- STI_growth_paired + stat_compare_means(paired = TRUE, label.x = 1.5) + rremove("legend")
STI_growth_paired
ok - also - lets look at these things in a bit more traditional setting:
sum_growth2STI_MR_vs_aLRL <- ggscatter(sum_growth2, x = "MR.STI", y = "aLRL.STI", color = "aLRL.delta.Control", size = 0.3,
rug = TRUE, add = "reg.line") + stat_cor(method = "pearson")
STI_MR_vs_aLRL <- STI_MR_vs_aLRL + gradient_color(c("blue", "grey", "red"))
STI_MR_vs_aLRL
STI_MR_vs_LRno <- ggscatter(sum_growth2, x = "MR.STI", y = "LRno.STI", color = "LRno.delta.Control", size = 0.3,
rug = TRUE, add = "reg.line") + stat_cor(method = "pearson")
STI_MR_vs_LRno <- STI_MR_vs_LRno + gradient_color(c("blue", "grey", "red"))
STI_MR_vs_LRno
STI_aLRL_vs_LRno <- ggscatter(sum_growth2, x = "aLRL.STI", y = "LRno.STI", color = "LRno.delta.Control", size = 0.3,
rug = TRUE, add = "reg.line") + stat_cor(method = "pearson")
STI_aLRL_vs_LRno <- STI_aLRL_vs_LRno + gradient_color(c("blue", "grey", "red"))
STI_aLRL_vs_LRno
Conclusions so far
OK - so all of the above is pretty interesting - from the paired STI graph we can tell that there are multiple classes of accessions:
STI_growth_paired
- accessions that are equally impaired for all components of RSA
- accessions that are LEAST impaired in MRL
- accessions that are MOST impaired in MRL
- accessions that are MOST impaired in LRno
The correlations between individual SITs is very low for all combinations - which is pretty exciting, and aLRL and MRL are in general the least impacted by the salt stress - while LRno is impaired in most accessions the most - based on this histogram:
STI_growth_histo
now - I am wondering - if we throw only the STI into the hierarchical clustering - would we be able to discern between these classes of accessions?
Let’s see:
Hclust based on the SITs:
First - let’s prepare the matrix:
colnames(sum_growth2) [1] "genotype" "MR.delta.Control" "LRno.delta.Control" "aLRL.delta.Control" "MR.delta.Salt"
[6] "LRno.delta.Salt" "aLRL.delta.Salt" "MR.STI" "LRno.STI" "aLRL.STI" STI_only <- sum_growth2[,c(1,8:10)]
# get only numeric variables from your data - NOT plant identifiers
STI_matrix <- STI_only[, c(2:4)]
# transfer all numeric variables into matrix
STI_matrix2 <- as.matrix(STI_matrix)
# add plant identifiers as row names
row.names(STI_matrix2) <- STI_only$genotype
# omit missing values - otherwise we wont be able to do the clustering
final_m <- na.omit(STI_matrix2)
dim(STI_matrix2)[1] 170 3dim(final_m)[1] 169 3head(final_m) MR.STI LRno.STI aLRL.STI
001 1.2207550 0.2886179 0.9878750
002 1.7812165 0.8893443 0.6813830
003 0.6241646 0.5460317 0.9047426
005 0.8284342 1.3500000 0.6491019
006 1.2725551 0.4566210 0.5787553
007 1.0796448 0.2659574 1.1628734# run a correlation
magda_cor = cor(final_m,method=c("pearson"))
# calculate distance
magda_dist = dist(magda_cor)
# this is the clustering itself - but now we are clustering how phenotypes are related to eachother
magda_clust = hclust(magda_dist,method="ward.D2")
plot(as.dendrogram(magda_clust),horiz=T)
# transposing the matrix
magda_t_matrix=t(final_m)
magda_t_cor = cor(magda_t_matrix,method=c("pearson"))
magda_t_dist = dist(magda_t_cor)
# clustering of accessions
magda_t_clust = hclust(magda_t_dist,method="ward.D2")
plot(as.dendrogram(magda_t_clust),horiz=T)
clusters <- cutree(magda_t_clust,h=24)
unique(clusters)[1] 1 2 3 4 5write.table(as.data.frame(cutree(magda_t_clust,h=24)),file="STI_clusters.txt",sep="\t",quote=F)
# the scaling only now applies to "row" which are our different phenotypes
heatmap.2(magda_t_matrix,Rowv=as.dendrogram(magda_clust),Colv=as.dendrogram(magda_t_clust),col=blue2red(100),scale=c("row"),density.info="none",trace="none")
OK - let’s save this one into a file before we forget
pdf("Fig.STI_clustering.pdf",width=12,height=7)
heatmap.2(magda_t_matrix,Rowv=as.dendrogram(magda_clust),Colv=as.dendrogram(magda_t_clust),col=blue2red(100),scale=c("row"),density.info="none",trace="none")
dev.off()null device
1 cool - now let’s see if the clusters actually differ in their respective growth rates / STIs.
First - let’s fuse “clusters” with the other growth rate data
clusters 001 002 003 005 006 007 008 009 010 011 012 013 028 029 030 031 032 033 034 035 036 037 038
1 1 2 3 1 4 2 4 1 2 4 4 4 4 1 5 2 5 1 4 1 2 2
040 055 056 057 058 059 060 061 062 063 065 066 074 075 076 078 079 080 081 082 083 084 085
4 4 2 1 1 5 4 4 2 3 5 5 2 4 4 1 1 1 4 1 1 2 1
086 087 088 089 090 091 092 093 094 095 096 097 098 099 101 102 103 104 105 106 136 138 139
1 1 2 2 5 1 1 1 2 2 1 2 2 2 1 2 1 1 4 4 2 4 2
140 141 142 143 144 146 147 165 166 167 168 169 170 171 172 173 174 175 176 191 192 193 194
2 1 1 1 1 2 5 1 4 2 2 4 4 4 5 2 5 1 2 2 4 2 1
195 196 197 198 199 200 201 207 211 212 213 214 220 221 222 225 227 228 229 230 231 233 234
4 2 2 2 5 5 5 2 2 2 1 4 1 1 2 1 5 4 4 5 4 4 3
235 236 237 239 240 241 244 245 247 248 249 250 251 252 253 255 256 258 259 260 261 262 264
1 1 1 4 1 4 5 4 1 2 5 1 2 2 1 2 1 1 5 2 1 1 3
267 268 269 270 271 272 273 274 277 278 279 280 282 283 284 285 286 287 288 290 291 292 293
3 1 1 1 4 2 1 1 2 1 4 2 2 2 2 1 4 1 4 1 1 4 1
294 298 299 300 301 306 307 308
1 1 5 2 5 1 5 1 sumatrix <- sum_growth2[,c(2:10)]
sumatrix2 <- as.matrix(sumatrix)
row.names(sumatrix2) <- sum_growth2$genotype
sumatrix3 <- na.omit(sumatrix2)
sum_growth3 <- cbind(sumatrix3, clusters)
head(sum_growth3) MR.delta.Control LRno.delta.Control aLRL.delta.Control MR.delta.Salt LRno.delta.Salt aLRL.delta.Salt MR.STI
001 1.2059372 6.150000 0.1881415 1.4721539 1.7750000 0.1858603 1.2207550
002 0.7807096 2.033333 0.6265680 1.3906129 1.8083333 0.4269328 1.7812165
003 1.2698976 5.250000 0.2862967 0.7926251 2.8666667 0.2590248 0.6241646
005 1.2294472 3.833333 0.4431234 1.0185161 5.1750000 0.2876322 0.8284342
006 1.2223445 7.300000 0.2833526 1.5555007 3.3333333 0.1639918 1.2725551
007 0.6433493 3.133333 0.3487621 0.6945888 0.8333333 0.4055662 1.0796448
LRno.STI aLRL.STI clusters
001 0.2886179 0.9878750 1
002 0.8893443 0.6813830 1
003 0.5460317 0.9047426 2
005 1.3500000 0.6491019 3
006 0.4566210 0.5787553 1
007 0.2659574 1.1628734 4ok - now let’s make some graphs per clusters starting with STIs:
growth_cluster3 <- as.data.frame(sum_growth3)
growth_cluster3
STIcluster_MR.STI <- ggerrorplot(growth_cluster3, y = "MR.STI", x = "clusters", fill="clusters",
color="clusters", desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"), xlab="",
ylab="Fraction of Control (Main Root Growth)", title = "Main Root STI")
STIcluster_MR.STI <- STIcluster_MR.STI + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1,
label = "p.signif", hide.ns = T)
STIcluster_MR.STI
STIcluster_LRno.STI <- ggerrorplot(growth_cluster3, y = "LRno.STI", x = "clusters", fill="clusters",
color="clusters", desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"), xlab="",
ylab="Fraction of Control (LR #)", title = "Lateral Root # STI")
STIcluster_LRno.STI <- STIcluster_LRno.STI + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1,
label = "p.signif", hide.ns = T)
STIcluster_LRno.STI
STIcluster_aLRL.STI <- ggerrorplot(growth_cluster3, y = "aLRL.STI", x = "clusters", fill="clusters",
color="clusters", desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"), xlab="",
ylab="Fraction of Control (avg. Lateral Root Growth)",
title = " avg Lateral Root Growth STI")
STIcluster_aLRL.STI <- STIcluster_aLRL.STI + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1,
label = "p.signif", hide.ns = T)
STIcluster_aLRL.STI
Let’s put these into one figure and think about it in another session:
pdf("Fig.STI_based_clusters_STI_trait_comparison.pdf", height = 15, width = 10)
plot_grid(STIcluster_MR.STI, STIcluster_LRno.STI, STIcluster_aLRL.STI, ncol=1, labels="AUTO")
dev.off()null device
1 I m still wondering how the correlations between the traits would look if we would color it per cluster now:
growth_cluster3$clusters <- as.factor(growth_cluster3$clusters)
STI_aLRL_vs_LRno_clust <- ggscatter(growth_cluster3, x = "aLRL.STI", y = "LRno.STI", color = "clusters", size = 0.3,
rug = TRUE, ellipse = TRUE, palette = "jco") + stat_cor(method = "pearson")
STI_aLRL_vs_LRno_clust
STI_aLRL_vs_MR_clust <- ggscatter(growth_cluster3, x = "aLRL.STI", y = "MR.STI", color = "clusters", size = 0.3,
rug = TRUE, ellipse = TRUE, palette = "jco") + stat_cor(method = "pearson")
STI_aLRL_vs_MR_clust
STI_LRno_vs_MR_clust <- ggscatter(growth_cluster3, x = "LRno.STI", y = "MR.STI", color = "clusters", size = 0.3,
rug = TRUE, ellipse = TRUE, palette = "jco") + stat_cor(method = "pearson")
STI_LRno_vs_MR_clust
I wonder if we could visualize accessions that belong to individual clusters in the continuous graphs with average per accession.
Let’s load the STI_cluster table:
cluster01 <- read.table("STI_clusters_better.txt", header = T)
head(cluster01)now let’s fuse it with the MR_sum dataset that we used previously for timeseries graphs:
head(MR_sum)MR_sum2 <- MR_sum
MR_sum2$genotype <- as.numeric(as.character(MR_sum2$genotype))
MR_cluster <- merge(MR_sum2, cluster01, by="genotype", all=T)
head(MR_cluster)MR_cluster$cluster <- as.factor(MR_cluster$cluster)Cool - now let’s see how the TRS graph looks like when we colour per cluster:
ggplotly(TRS_clust_lgraph)
`group_by_()` is deprecated as of dplyr 0.7.0.
Please use `group_by()` instead.
See vignette('programming') for more help
[90mThis warning is displayed once every 8 hours.[39m
[90mCall `lifecycle::last_warnings()` to see where this warning was generated.[39mSTIcluster_MR.C <- ggerrorplot(growth_cluster3, y = "MR.delta.Control", x = "clusters", fill="clusters",
color="clusters", desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"), xlab="Clusters based on STI",
ylab="Main Root Growth (cm / day)", title = "Control")
STIcluster_MR.C <- STIcluster_MR.C + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1,
label = "p.signif", hide.ns = T)
STIcluster_MR.C
STIcluster_MR.S <- ggerrorplot(growth_cluster3, y = "MR.delta.Salt", x = "clusters", fill="clusters",
color="clusters", desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"), xlab="Clusters based on STI",
ylab="Main Root Growth (cm / day)", title = "Salt")
STIcluster_MR.S <- STIcluster_MR.S + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1,
label = "p.signif", hide.ns = T)
STIcluster_MR.S
STIcluster_aLR.C <- ggerrorplot(growth_cluster3, y = "aLRL.delta.Control", x = "clusters", fill="clusters",
color="clusters", desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"), xlab="Clusters based on STI",
ylab="avg Lateral Root Growth (cm / day)", title = "Control")
STIcluster_aLR.C <- STIcluster_aLR.C + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1,
label = "p.signif", hide.ns = T)
STIcluster_aLR.C
STIcluster_aLR.S <- ggerrorplot(growth_cluster3, y = "aLRL.delta.Salt", x = "clusters", fill="clusters",
color="clusters", desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"), xlab="Clusters based on STI",
ylab="avg Lateral Root Growth (cm / day)", title = "Salt")
STIcluster_aLR.S <- STIcluster_aLR.S + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1,
label = "p.signif", hide.ns = T)
STIcluster_aLR.S
STIcluster_LRno.C <- ggerrorplot(growth_cluster3, y = "LRno.delta.Control", x = "clusters", fill="clusters", id="genotype",
color="clusters", desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"), xlab="Clusters based on STI",
ylab="Lateral Root Increase (# LR / day)", title = "Control")
STIcluster_LRno.C <- STIcluster_LRno.C + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1,
label = "p.signif", hide.ns = T)
STIcluster_LRno.C
library(plotly)
ggplotly(STIcluster_LRno.C)STIcluster_LRno.S <- ggerrorplot(growth_cluster3, y = "LRno.delta.Salt", x = "clusters", fill="clusters",
color="clusters", desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"), xlab="Clusters based on STI",
ylab="Lateral Root Increase (# LR / day)", title = "Salt")
STIcluster_LRno.S <- STIcluster_LRno.S + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1,
label = "p.signif", hide.ns = T)
STIcluster_LRno.S
Let’s have a look and select accessions:
tomato$genotype
[1] " 002" " 008" " 012" " 013" " 063" " 075" " 084" " 086" " 087" " 088" " 091" " 092" " 094" " 095" " 096" " 101"
[17] " 104" " 105" " 106" " 139" " 144" " 146" " 147" " 165" " 167" " 168" " 169" " 171" " 173" " 192" " 197" " 230"
[33] " 241" " 245" " 248" " 250" " 253" " 255" " 280" " 292"that’s of course based on the mean - lets have a look at the most interesting accessions tho:
interesting <- subset(all_MR3, all_MR3$genotype == " 002")
interesting
M002_lgraph <- ggplot(data=interesting, aes(x= DAS, y=TRS, group = root_name, color = cond))
M002_lgraph <- M002_lgraph +geom_line(alpha = 0.1)
M002_lgraph <- M002_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
M002_lgraph <- M002_lgraph + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.M002_lgraph <- M002_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
M002_lgraph <- M002_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("M002")
M002_lgraph <- M002_lgraph + ylab("Total Root Size (cm)") + xlab("Days After Stress") + theme(legend.position='none')
M002_lgraph
interesting <- subset(all_MR3, all_MR3$genotype == " 248")
interesting
M248_lgraph <- ggplot(data=interesting, aes(x= DAS, y=TRS, group = root_name, color = cond))
M248_lgraph <- M248_lgraph +geom_line(alpha = 0.1)
M248_lgraph <- M248_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
M248_lgraph <- M248_lgraph + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.M248_lgraph <- M248_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
M248_lgraph <- M248_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("M248")
M248_lgraph <- M248_lgraph + ylab("Total Root Size (cm)") + xlab("Days After Stress") + theme(legend.position='none')
M248_lgraph
Pareto and RSA
OK - so now let’s get to the actual paretho calculations made by Arjun.
getwd()[1] "/Users/magdalena/Dropbox/DataAndAnalysis/Tomato/PimpiBIG csv raw files"Pareto_01 <- read.csv("/Users/magdalena/Dropbox/DataAndAnalysis/Tomato/PimpiBIG csv raw files/Arjun_Paretho/arbor_stats.csv")
Pareto_01Pareto_02 <- read.csv("/Users/magdalena/Dropbox/DataAndAnalysis/Tomato/PimpiBIG csv raw files/Arjun_Paretho/scaling_distances.csv")
Pareto_02head(all_MR3)meta <- read.csv("/Users/magdalena/Dropbox/DataAndAnalysis/Tomato/PimpiBIG csv raw files/Arjun_Paretho/metadata.csv")
metaCool - let’s try to fuse it all together:
dim(Pareto_01)[1] 4219 3dim(Pareto_02)[1] 4218 5dim(meta)[1] 5559 7dim(all_MR3)[1] 5507 64ok - so for some reason we do have less paretho calculated than actually plants, but that’s ok at this point.
Let’s first fuse two paretho files:
Then add meta-data
unique(Pareto_all$arbor.name) [1] "001_1_C_day2" "001_1_C_day3" "001_1_C_day4" "001_1_C_day5" "001_1_S_day2" "001_1_S_day3" "001_1_S_day4"
[8] "001_1_S_day5" "001_2_C_day2" "001_2_C_day3" "001_2_C_day4" "001_2_C_day5" "001_2_S_day2" "001_2_S_day3"
[15] "001_2_S_day4" "001_2_S_day5" "001_3_C_day1" "001_3_C_day2" "001_3_C_day3" "001_3_C_day4" "001_3_C_day5"
[22] "001_3_S_day2" "001_3_S_day3" "001_3_S_day4" "001_3_S_day5" "001_4_C_day2" "001_4_C_day3" "001_4_C_day4"
[29] "001_4_C_day5" "001_4_S_day2" "001_4_S_day3" "001_4_S_day4" "001_4_S_day5" "002_1_C_day2" "002_1_C_day3"
[36] "002_1_C_day4" "002_1_C_day5" "002_1_S_day1" "002_1_S_day2" "002_1_S_day3" "002_1_S_day4" "002_1_S_day5"
[43] "002_2_C_day2" "002_2_C_day3" "002_2_C_day4" "002_2_C_day5" "002_2_S_day2" "002_2_S_day3" "002_2_S_day4"
[50] "002_2_S_day5" "002_3_C_day2" "002_3_C_day3" "002_3_C_day4" "002_3_C_day5" "002_3_S_day2" "002_3_S_day3"
[57] "002_3_S_day4" "002_3_S_day5" "002_4_S_day2" "002_4_S_day3" "002_4_S_day4" "002_4_S_day5" "003_1_C_day2"
[64] "003_1_C_day3" "003_1_C_day4" "003_1_C_day5" "003_1_S_day3" "003_1_S_day4" "003_1_S_day5" "003_2_S_day2"
[71] "003_2_S_day3" "003_2_S_day4" "003_2_S_day5" "003_3_C_day2" "003_3_C_day3" "003_3_C_day4" "003_3_C_day5"
[78] "003_3_S_day2" "003_3_S_day3" "003_3_S_day4" "003_3_S_day5" "005_1_S_day2" "005_1_S_day3" "005_1_S_day4"
[85] "005_1_S_day5" "005_2_C_day2" "005_2_C_day3" "005_2_C_day4" "005_2_C_day5" "005_2_S_day4" "005_2_S_day5"
[92] "005_3_C_day2" "005_3_C_day3" "005_3_C_day4" "005_3_C_day5" "005_3_S_day4" "005_3_S_day5" "005_4_C_day2"
[99] "005_4_C_day3" "005_4_C_day4" "005_4_C_day5" "005_4_S_day3" "005_4_S_day4" "005_4_S_day5" "006_1_C_day2"
[106] "006_1_C_day3" "006_1_C_day4" "006_1_C_day5" "006_1_S_day3" "006_1_S_day4" "006_1_S_day5" "006_2_C_day2"
[113] "006_2_C_day3" "006_2_C_day4" "006_2_C_day5" "006_2_S_day4" "006_2_S_day5" "006_3_C_day2" "006_3_C_day3"
[120] "006_3_C_day4" "006_3_C_day5" "006_3_S_day3" "006_3_S_day4" "006_3_S_day5" "007_1_C_day2" "007_1_C_day3"
[127] "007_1_C_day4" "007_1_C_day5" "007_1_S_day2" "007_1_S_day3" "007_1_S_day4" "007_1_S_day5" "007_2_C_day2"
[134] "007_2_C_day3" "007_2_C_day4" "007_2_C_day5" "007_2_S_day2" "007_2_S_day3" "007_2_S_day4" "007_2_S_day5"
[141] "007_3_C_day2" "007_3_C_day3" "007_3_C_day4" "007_3_C_day5" "007_3_S_day2" "007_3_S_day3" "007_3_S_day4"
[148] "007_3_S_day5" "008_1_C_day2" "008_1_C_day3" "008_1_C_day4" "008_1_C_day5" "008_1_S_day2" "008_1_S_day3"
[155] "008_1_S_day4" "008_1_S_day5" "008_2_C_day2" "008_2_C_day3" "008_2_C_day4" "008_2_C_day5" "008_2_S_day2"
[162] "008_2_S_day3" "008_2_S_day4" "008_2_S_day5" "008_3_C_day1" "008_3_C_day2" "008_3_C_day3" "008_3_C_day4"
[169] "008_3_C_day5" "008_3_S_day2" "008_3_S_day3" "008_3_S_day4" "008_3_S_day5" "008_4_C_day2" "008_4_C_day3"
[176] "008_4_C_day4" "008_4_C_day5" "008_4_S_day2" "008_4_S_day3" "008_4_S_day4" "008_4_S_day5" "009_1_C_day1"
[183] "009_1_C_day2" "009_1_C_day3" "009_1_C_day4" "009_1_C_day5" "009_1_S_day2" "009_1_S_day3" "009_1_S_day4"
[190] "009_1_S_day5" "009_2_C_day2" "009_2_C_day3" "009_2_C_day4" "009_2_C_day5" "009_2_S_day2" "009_2_S_day3"
[197] "009_2_S_day4" "009_2_S_day5" "009_3_C_day2" "009_3_C_day3" "009_3_C_day4" "009_3_C_day5" "009_3_S_day2"
[204] "009_3_S_day3" "009_3_S_day4" "009_3_S_day5" "009_4_C_day2" "009_4_C_day3" "009_4_C_day4" "009_4_C_day5"
[211] "009_4_S_day1" "009_4_S_day2" "009_4_S_day3" "009_4_S_day4" "009_4_S_day5" "010_1_C_day4" "010_1_C_day5"
[218] "010_2_C_day3" "010_2_C_day4" "010_2_C_day5" "010_3_C_day4" "010_3_C_day5" "010_3_S_day1" "010_3_S_day5"
[225] "011_1_C_day2" "011_1_C_day3" "011_1_C_day4" "011_1_C_day5" "011_1_S_day3" "011_1_S_day4" "011_1_S_day5"
[232] "011_2_C_day2" "011_2_C_day3" "011_2_C_day4" "011_2_C_day5" "011_2_S_day2" "011_2_S_day3" "011_2_S_day4"
[239] "011_2_S_day5" "011_3_C_day2" "011_3_C_day3" "011_3_C_day4" "011_3_C_day5" "011_3_S_day2" "011_3_S_day3"
[246] "011_3_S_day4" "011_3_S_day5" "011_4_C_day1" "011_4_C_day2" "011_4_C_day3" "011_4_C_day4" "011_4_C_day5"
[253] "011_4_S_day4" "011_4_S_day5" "012_1_C_day2" "012_1_C_day3" "012_1_C_day4" "012_1_C_day5" "012_1_S_day1"
[260] "012_1_S_day2" "012_1_S_day3" "012_1_S_day4" "012_1_S_day5" "012_2_C_day2" "012_2_C_day3" "012_2_C_day4"
[267] "012_2_C_day5" "012_2_S_day2" "012_2_S_day3" "012_2_S_day4" "012_2_S_day5" "012_3_C_day2" "012_3_C_day3"
[274] "012_3_C_day4" "012_3_C_day5" "012_3_S_day1" "012_3_S_day2" "012_3_S_day3" "012_3_S_day4" "012_3_S_day5"
[281] "012_4_C_day2" "012_4_C_day3" "012_4_C_day4" "012_4_C_day5" "012_4_S_day2" "012_4_S_day4" "012_4_S_day5"
[288] "013_1_C_day2" "013_1_C_day3" "013_1_C_day4" "013_1_C_day5" "013_1_S_day2" "013_1_S_day3" "013_1_S_day4"
[295] "013_1_S_day5" "013_2_C_day1" "013_2_C_day2" "013_2_C_day3" "013_2_C_day4" "013_2_C_day5" "013_2_S_day2"
[302] "013_2_S_day3" "013_2_S_day4" "013_2_S_day5" "013_3_C_day1" "013_3_C_day2" "013_3_C_day3" "013_3_C_day4"
[309] "013_3_C_day5" "013_3_S_day2" "013_3_S_day3" "013_3_S_day4" "013_3_S_day5" "022_4_C_day2" "022_4_C_day3"
[316] "022_4_C_day4" "022_4_C_day5" "028_1_C_day2" "028_1_C_day3" "028_1_C_day4" "028_1_C_day5" "028_1_S_day2"
[323] "028_1_S_day3" "028_1_S_day4" "028_1_S_day5" "028_2_C_day1" "028_2_C_day2" "028_2_C_day3" "028_2_C_day4"
[330] "028_2_C_day5" "028_2_S_day2" "028_2_S_day3" "028_2_S_day4" "028_3_C_day2" "028_3_C_day3" "028_3_C_day4"
[337] "028_3_C_day5" "028_3_S_day2" "028_3_S_day3" "028_3_S_day4" "028_3_S_day5" "028_4_C_day2" "028_4_C_day3"
[344] "028_4_C_day4" "028_4_C_day5" "028_4_S_day3" "028_4_S_day4" "028_4_S_day5" "029_1_C_day2" "029_1_C_day3"
[351] "029_1_C_day4" "029_1_C_day5" "029_1_S_day3" "029_1_S_day4" "029_1_S_day5" "029_2_C_day2" "029_2_C_day3"
[358] "029_2_C_day4" "029_2_C_day5" "029_2_S_day4" "029_2_S_day5" "029_3_C_day2" "029_3_C_day3" "029_3_C_day4"
[365] "029_3_C_day5" "029_3_S_day2" "029_3_S_day3" "029_3_S_day4" "029_3_S_day5" "029_4_C_day2" "029_4_C_day3"
[372] "029_4_C_day4" "029_4_C_day5" "029_4_S_day2" "029_4_S_day3" "029_4_S_day4" "029_4_S_day5" "030_1_C_day1"
[379] "030_1_C_day2" "030_1_C_day3" "030_1_C_day4" "030_1_C_day5" "030_1_S_day2" "030_1_S_day3" "030_1_S_day4"
[386] "030_1_S_day5" "030_2_C_day2" "030_2_C_day3" "030_2_C_day4" "030_2_C_day5" "030_2_S_day2" "030_2_S_day3"
[393] "030_2_S_day4" "030_2_S_day5" "030_3_C_day2" "030_3_C_day3" "030_3_C_day4" "030_3_C_day5" "030_3_S_day2"
[400] "030_3_S_day3" "030_3_S_day4" "030_3_S_day5" "031_1_C_day2" "031_1_C_day3" "031_1_C_day4" "031_1_C_day5"
[407] "031_1_S_day2" "031_1_S_day3" "031_1_S_day4" "031_1_S_day5" "031_2_C_day2" "031_2_C_day3" "031_2_C_day4"
[414] "031_2_C_day5" "031_2_S_day3" "031_2_S_day4" "031_2_S_day5" "031_3_C_day2" "031_3_C_day3" "031_3_C_day4"
[421] "031_3_C_day5" "031_3_S_day3" "031_3_S_day4" "031_3_S_day5" "031_4_C_day2" "031_4_C_day3" "031_4_C_day4"
[428] "031_4_C_day5" "031_4_S_day2" "031_4_S_day3" "031_4_S_day4" "031_4_S_day5" "032_1_C_day2" "032_1_C_day3"
[435] "032_1_C_day4" "032_1_C_day5" "032_1_S_day4" "032_1_S_day5" "032_2_C_day2" "032_2_C_day3" "032_2_C_day4"
[442] "032_2_C_day5" "032_2_S_day2" "032_2_S_day3" "032_2_S_day4" "032_2_S_day5" "032_3_C_day2" "032_3_C_day3"
[449] "032_3_C_day4" "032_3_C_day5" "032_3_S_day2" "032_3_S_day3" "032_3_S_day4" "032_3_S_day5" "032_4_C_day2"
[456] "032_4_C_day3" "032_4_C_day4" "032_4_C_day5" "032_4_S_day2" "032_4_S_day3" "032_4_S_day4" "032_4_S_day5"
[463] "033_1_C_day2" "033_1_C_day3" "033_1_C_day4" "033_1_C_day5" "033_1_S_day4" "033_1_S_day5" "033_2_C_day2"
[470] "033_2_C_day3" "033_2_C_day4" "033_2_C_day5" "033_2_S_day4" "033_2_S_day5" "033_3_C_day3" "033_3_C_day4"
[477] "033_3_C_day5" "033_3_S_day4" "033_3_S_day5" "034_1_C_day2" "034_1_C_day3" "034_1_C_day4" "034_1_C_day5"
[484] "034_1_S_day4" "034_1_S_day5" "034_2_C_day2" "034_2_C_day3" "034_2_C_day4" "034_2_C_day5" "034_2_S_day5"
[491] "034_3_C_day2" "034_3_C_day3" "034_3_C_day4" "034_3_C_day5" "034_3_S_day5" "035_1_C_day2" "035_1_C_day3"
[498] "035_1_C_day4" "035_1_C_day5" "035_1_S_day1" "035_1_S_day2" "035_1_S_day3" "035_1_S_day4" "035_1_S_day5"
[505] "035_2_C_day2" "035_2_C_day3" "035_2_C_day4" "035_2_C_day5" "035_2_S_day2" "035_2_S_day3" "035_2_S_day4"
[512] "035_2_S_day5" "035_3_C_day2" "035_3_C_day3" "035_3_C_day4" "035_3_C_day5" "035_3_S_day3" "035_3_S_day4"
[519] "035_3_S_day5" "035_4_C_day2" "035_4_C_day3" "035_4_C_day4" "035_4_C_day5" "035_4_S_day2" "035_4_S_day3"
[526] "035_4_S_day4" "035_4_S_day5" "036_1_C_day3" "036_1_C_day4" "036_1_C_day5" "036_1_S_day3" "036_1_S_day4"
[533] "036_1_S_day5" "036_2_C_day2" "036_2_C_day3" "036_2_C_day4" "036_2_C_day5" "036_2_S_day4" "036_2_S_day5"
[540] "037_1_C_day3" "037_1_C_day4" "037_1_C_day5" "037_1_S_day4" "037_1_S_day5" "038_1_C_day2" "038_1_C_day3"
[547] "038_1_C_day4" "038_1_C_day5" "038_1_S_day2" "038_1_S_day3" "038_1_S_day4" "038_1_S_day5" "038_2_C_day2"
[554] "038_2_C_day3" "038_2_C_day4" "038_2_C_day5" "038_2_S_day1" "038_2_S_day2" "038_2_S_day3" "038_2_S_day4"
[561] "038_2_S_day5" "038_3_C_day1" "038_3_C_day2" "038_3_C_day3" "038_3_C_day4" "038_3_C_day5" "038_3_S_day2"
[568] "038_3_S_day3" "038_3_S_day4" "038_3_S_day5" "038_4_C_day1" "038_4_C_day2" "038_4_C_day3" "038_4_C_day4"
[575] "038_4_C_day5" "038_4_S_day2" "038_4_S_day3" "038_4_S_day4" "038_4_S_day5" "040_1_C_day2" "040_1_C_day3"
[582] "040_1_C_day4" "040_1_C_day5" "040_1_S_day3" "040_1_S_day4" "040_1_S_day5" "040_2_C_day2" "040_2_C_day3"
[589] "040_2_C_day4" "040_2_C_day5" "040_2_S_day2" "040_2_S_day3" "040_2_S_day4" "040_2_S_day5" "040_3_C_day2"
[596] "040_3_C_day3" "040_3_C_day4" "040_3_C_day5" "040_3_S_day2" "040_3_S_day3" "040_3_S_day4" "040_3_S_day5"
[603] "040_4_C_day2" "040_4_C_day3" "040_4_C_day4" "040_4_C_day5" "040_4_S_day2" "040_4_S_day3" "040_4_S_day4"
[610] "040_4_S_day5" "055_1_C_day2" "055_1_C_day3" "055_1_C_day4" "055_1_C_day5" "055_1_S_day2" "055_1_S_day3"
[617] "055_1_S_day4" "055_1_S_day5" "055_2_C_day2" "055_2_C_day3" "055_2_C_day4" "055_2_C_day5" "055_2_S_day2"
[624] "055_2_S_day3" "055_2_S_day4" "055_2_S_day5" "055_3_C_day2" "055_3_C_day3" "055_3_C_day4" "055_3_C_day5"
[631] "055_3_S_day2" "055_3_S_day3" "055_3_S_day4" "055_3_S_day5" "056_1_C_day2" "056_1_C_day3" "056_1_C_day4"
[638] "056_1_C_day5" "056_1_S_day4" "056_1_S_day5" "056_2_C_day2" "056_2_C_day3" "056_2_C_day4" "056_2_C_day5"
[645] "056_2_S_day1" "056_2_S_day2" "056_2_S_day3" "056_2_S_day4" "056_2_S_day5" "056_3_C_day1" "056_3_C_day2"
[652] "056_3_C_day3" "056_3_C_day4" "056_3_C_day5" "056_3_S_day2" "056_3_S_day3" "056_3_S_day4" "056_3_S_day5"
[659] "057_1_C_day2" "057_1_C_day3" "057_1_C_day4" "057_1_C_day5" "057_1_S_day2" "057_1_S_day3" "057_1_S_day4"
[666] "057_1_S_day5" "057_2_C_day2" "057_2_C_day3" "057_2_C_day4" "057_2_C_day5" "057_2_S_day1" "057_2_S_day2"
[673] "057_2_S_day3" "057_2_S_day4" "057_2_S_day5" "057_3_C_day2" "057_3_C_day3" "057_3_C_day4" "057_3_C_day5"
[680] "057_3_S_day2" "057_3_S_day3" "057_3_S_day4" "057_3_S_day5" "057_4_C_day2" "057_4_C_day3" "057_4_C_day4"
[687] "057_4_C_day5" "057_4_S_day2" "057_4_S_day3" "057_4_S_day4" "057_4_S_day5" "058_1_C_day2" "058_1_C_day3"
[694] "058_1_C_day4" "058_1_C_day5" "058_2_C_day2" "058_2_C_day3" "058_2_C_day4" "058_2_C_day5" "058_3_C_day2"
[701] "058_3_C_day3" "058_3_C_day4" "058_3_C_day5" "058_4_C_day2" "058_4_C_day3" "058_4_C_day4" "058_4_C_day5"
[708] "059_1_C_day1" "059_1_C_day2" "059_1_C_day3" "059_1_S_day2" "059_1_S_day3" "059_1_S_day4" "059_1_S_day5"
[715] "059_2_C_day2" "059_2_C_day3" "059_2_C_day4" "059_2_C_day5" "059_2_S_day1" "059_2_S_day3" "059_2_S_day4"
[722] "059_2_S_day5" "059_3_C_day2" "059_3_C_day3" "059_3_C_day4" "059_3_C_day5" "059_3_S_day4" "059_3_S_day5"
[729] "059_4_C_day2" "059_4_C_day3" "059_4_C_day4" "059_4_C_day5" "059_4_S_day2" "059_4_S_day3" "059_4_S_day4"
[736] "059_4_S_day5" "060_1_C_day2" "060_1_C_day3" "060_1_C_day4" "060_1_C_day5" "060_1_S_day4" "060_1_S_day5"
[743] "060_2_C_day2" "060_2_C_day3" "060_2_C_day4" "060_2_C_day5" "060_2_S_day2" "060_2_S_day3" "060_2_S_day4"
[750] "060_2_S_day5" "060_3_C_day1" "060_3_C_day2" "060_3_C_day3" "060_3_C_day4" "060_3_C_day5" "060_3_S_day2"
[757] "060_3_S_day3" "060_3_S_day4" "060_3_S_day5" "061_1_C_day1" "061_1_C_day2" "061_1_C_day3" "061_1_C_day4"
[764] "061_1_C_day5" "061_1_S_day4" "061_1_S_day5" "061_2_C_day2" "061_2_C_day3" "061_2_C_day4" "061_2_C_day5"
[771] "061_2_S_day4" "061_2_S_day5" "061_3_C_day2" "061_3_C_day3" "061_3_C_day4" "061_3_C_day5" "061_3_S_day2"
[778] "061_3_S_day3" "061_3_S_day4" "061_3_S_day5" "062_1_C_day2" "062_1_C_day3" "062_1_C_day4" "062_1_C_day5"
[785] "062_2_S_day3" "062_2_S_day4" "062_2_S_day5" "063_1_C_day1" "063_1_C_day2" "063_1_C_day3" "063_1_C_day4"
[792] "063_1_C_day5" "063_1_S_day2" "063_1_S_day3" "063_1_S_day4" "063_1_S_day5" "064_1_C_day1" "064_1_C_day2"
[799] "064_1_C_day3" "064_1_C_day4" "064_1_C_day5" "064_1_S_day3" "064_1_S_day4" "064_1_S_day5" "064_2_C_day3"
[806] "064_2_C_day4" "064_2_C_day5" "064_2_S_day5" "064_3_C_day2" "064_3_C_day3" "064_3_C_day5" "064_3_S_day2"
[813] "064_3_S_day3" "064_3_S_day4" "064_3_S_day5" "065_1_C_day1" "065_1_C_day2" "065_1_C_day3" "065_1_C_day4"
[820] "065_1_C_day5" "065_1_S_day4" "065_1_S_day5" "065_2_C_day2" "065_2_C_day3" "065_2_C_day4" "065_2_C_day5"
[827] "065_2_S_day2" "065_2_S_day3" "065_2_S_day4" "065_2_S_day5" "065_3_C_day2" "065_3_C_day3" "065_3_C_day4"
[834] "065_3_C_day5" "065_3_S_day1" "065_3_S_day2" "065_3_S_day3" "065_3_S_day4" "065_3_S_day5" "066_1_C_day2"
[841] "066_1_C_day3" "066_1_C_day4" "066_1_C_day5" "066_1_S_day4" "066_1_S_day5" "066_2_C_day2" "066_2_C_day3"
[848] "066_2_C_day4" "066_2_C_day5" "066_2_S_day4" "066_2_S_day5" "074_1_C_day2" "074_1_C_day3" "074_1_C_day4"
[855] "074_1_C_day5" "074_1_S_day2" "074_1_S_day3" "074_1_S_day4" "074_1_S_day5" "074_2_C_day2" "074_2_C_day3"
[862] "074_2_C_day4" "074_2_C_day5" "074_2_S_day2" "074_2_S_day3" "074_2_S_day4" "074_2_S_day5" "074_3_C_day2"
[869] "074_3_C_day3" "074_3_C_day4" "074_3_C_day5" "074_3_S_day2" "074_3_S_day3" "074_3_S_day4" "074_3_S_day5"
[876] "074_4_C_day2" "074_4_C_day3" "074_4_C_day4" "074_4_C_day5" "074_4_S_day2" "074_4_S_day3" "074_4_S_day4"
[883] "074_4_S_day5" "075_1_C_day2" "075_1_C_day3" "075_1_C_day4" "075_1_C_day5" "075_1_S_day2" "075_1_S_day3"
[890] "075_1_S_day4" "075_1_S_day5" "075_2_C_day2" "075_2_C_day3" "075_2_C_day4" "075_2_C_day5" "075_2_S_day1"
[897] "075_2_S_day2" "075_2_S_day3" "075_2_S_day4" "075_2_S_day5" "075_3_C_day2" "075_3_C_day3" "075_3_C_day4"
[904] "075_3_C_day5" "075_3_S_day2" "075_3_S_day3" "075_3_S_day4" "075_3_S_day5" "076_1_C_day2" "076_1_C_day3"
[911] "076_1_C_day4" "076_1_C_day5" "076_1_S_day1" "076_1_S_day2" "076_1_S_day3" "076_1_S_day4" "076_1_S_day5"
[918] "076_2_C_day2" "076_2_C_day3" "076_2_C_day4" "076_2_C_day5" "076_2_S_day1" "076_2_S_day2" "076_2_S_day3"
[925] "076_2_S_day4" "076_2_S_day5" "076_3_C_day2" "076_3_C_day3" "076_3_C_day4" "076_3_C_day5" "076_3_S_day1"
[932] "076_3_S_day2" "076_3_S_day3" "076_3_S_day4" "076_3_S_day5" "076_4_C_day1" "076_4_C_day2" "076_4_C_day3"
[939] "076_4_C_day4" "076_4_C_day5" "076_4_S_day4" "076_4_S_day5" "078_1_C_day2" "078_1_C_day3" "078_1_C_day4"
[946] "078_1_C_day5" "078_1_S_day2" "078_1_S_day3" "078_1_S_day4" "078_1_S_day5" "078_2_C_day3" "078_2_C_day4"
[953] "078_2_C_day5" "078_3_S_day3" "078_3_S_day4" "078_3_S_day5" "079_1_C_day2" "079_1_C_day3" "079_1_C_day4"
[960] "079_1_C_day5" "079_1_S_day3" "079_1_S_day4" "079_1_S_day5" "079_2_C_day2" "079_2_C_day3" "079_2_C_day4"
[967] "079_2_C_day5" "079_2_S_day3" "079_2_S_day4" "079_2_S_day5" "079_3_C_day2" "079_3_C_day3" "079_3_C_day4"
[974] "079_3_C_day5" "079_3_S_day2" "079_3_S_day3" "079_3_S_day4" "079_3_S_day5" "079_4_C_day2" "079_4_C_day3"
[981] "079_4_C_day4" "079_4_C_day5" "079_4_S_day2" "079_4_S_day3" "079_4_S_day4" "079_4_S_day5" "080_1_C_day2"
[988] "080_1_C_day3" "080_1_C_day4" "080_1_C_day5" "080_1_S_day3" "080_1_S_day4" "080_1_S_day5" "080_2_C_day2"
[995] "080_2_C_day3" "080_2_C_day4" "080_2_C_day5" "080_2_S_day3" "080_2_S_day4" "080_2_S_day5"
[ reached getOption("max.print") -- omitted 3218 entries ]unique(meta$arbor.name) [1] "103_4_S_day5" "031_3_S_day5" "055_2_S_day5" "098_1_S_day5" "101_1_S_day5" "059_4_C_day5" "034_1_C_day5"
[8] "030_2_S_day5" "002_1_C_day5" "001_3_C_day5" "076_1_C_day5" "102_4_C_day5" "104_3_S_day5" "080_2_S_day5"
[15] "080_1_S_day5" "005_3_S_day5" "081_1_C_day5" "096_1_C_day5" "101_2_C_day5" "028_4_C_day5" "058_4_C_day5"
[22] "009_2_C_day5" "038_3_C_day5" "079_4_C_day5" "080_4_S_day5" "040_1_C_day5" "035_4_C_day5" "040_1_S_day5"
[29] "094_3_C_day5" "074_4_C_day5" "010_2_C_day5" "103_1_S_day5" "032_4_S_day5" "103_1_C_day5" "102_2_S_day5"
[36] "010_1_S_day5" "022_4_C_day5" "003_3_S_day5" "105_3_C_day5" "056_2_S_day5" "009_3_S_day5" "095_1_S_day5"
[43] "002_2_S_day5" "011_4_S_day5" "106_1_S_day5" "040_3_S_day5" "098_3_C_day5" "001_4_S_day5" "075_2_C_day5"
[50] "103_2_S_day5" "009_2_S_day5" "028_3_C_day5" "081_1_S_day5" "012_1_S_day5" "033_2_C_day5" "098_3_S_day5"
[57] "079_2_S_day5" "058_4_S_day5" "097_1_S_day5" "013_3_C_day5" "031_1_S_day5" "094_2_S_day5" "013_2_S_day5"
[64] "006_3_S_day5" "007_1_C_day5" "057_1_S_day5" "008_1_S_day5" "076_3_C_day5" "008_3_C_day5" "012_3_S_day5"
[71] "099_4_C_day5" "094_3_S_day5" "057_3_C_day5" "035_2_S_day5" "099_1_C_day5" "029_4_S_day5" "012_2_S_day5"
[78] "030_2_C_day5" "105_3_S_day5" "012_4_S_day5" "007_2_S_day5" "032_1_C_day5" "013_1_C_day5" "038_2_S_day5"
[85] "010_3_S_day5" "078_3_S_day5" "058_2_S_day5" "099_1_S_day5" "001_1_C_day5" "057_1_C_day5" "098_4_S_day5"
[92] "057_2_S_day5" "080_1_C_day5" "006_3_C_day5" "029_3_S_day5" "094_2_C_day5" "056_3_C_day5" "028_1_S_day5"
[99] "095_4_S_day5" "074_3_C_day5" "040_2_C_day5" "105_1_S_day5" "001_2_S_day5" "003_3_C_day5" "075_3_S_day5"
[106] "007_2_C_day5" "031_2_C_day5" "035_2_C_day5" "074_4_S_day5" "059_3_C_day5" "007_3_C_day5" "001_2_C_day5"
[113] "106_2_C_day5" "007_1_S_day5" "104_2_S_day5" "040_4_C_day5" "002_1_S_day5" "005_3_C_day5" "079_3_S_day5"
[120] "103_4_C_day5" "009_1_C_day5" "096_2_S_day5" "032_2_C_day5" "099_2_S_day5" "055_2_C_day5" "057_4_S_day5"
[127] "075_3_C_day5" "028_3_S_day5" "007_3_S_day5" "095_4_C_day5" "081_4_S_day5" "081_3_C_day5" "056_3_S_day5"
[134] "103_3_S_day5" "037_1_C_day5" "059_2_C_day5" "012_3_C_day5" "099_3_C_day5" "038_1_S_day5" "079_1_C_day5"
[141] "038_4_C_day5" "031_4_C_day5" "102_3_C_day5" "006_2_C_day5" "059_3_S_day5" "101_2_S_day5" "057_2_C_day5"
[148] "103_3_C_day5" "104_3_C_day5" "057_4_C_day5" "029_3_C_day5" "105_4_S_day5" "030_1_S_day5" "057_3_S_day5"
[155] "058_1_C_day5" "036_2_C_day5" "030_1_C_day5" "010_2_S_day5" "038_2_C_day5" "011_1_C_day5" "032_3_S_day5"
[162] "035_1_S_day5" "078_1_C_day5" "056_2_C_day5" "029_1_C_day5" "101_3_S_day5" "081_2_S_day5" "055_1_S_day5"
[169] "005_2_S_day5" "034_1_S_day5" "008_1_C_day5" "011_3_C_day5" "055_3_C_day5" "001_1_S_day5" "105_4_C_day5"
[176] "003_2_S_day5" "102_4_S_day5" "031_4_S_day5" "079_3_C_day5" "013_2_C_day5" "059_2_S_day5" "010_1_C_day5"
[183] "008_3_S_day5" "104_1_S_day5" "010_3_C_day5" "011_4_C_day5" "058_2_C_day5" "099_4_S_day5" "075_1_C_day5"
[190] "080_2_C_day5" "009_1_S_day5" "033_2_S_day5" "099_2_C_day5" "038_3_S_day5" "011_3_S_day5" "055_3_S_day5"
[197] "080_3_S_day5" "028_1_C_day5" "035_3_C_day5" "008_2_S_day5" "094_1_C_day5" "056_1_S_day5" "030_3_C_day5"
[204] "035_3_S_day5" "005_4_C_day5" "058_3_S_day5" "038_1_C_day5" "056_1_C_day5" "079_4_S_day5" "031_1_C_day5"
[211] "012_2_C_day5" "095_3_S_day5" "105_1_C_day5" "033_3_S_day5" "097_1_C_day5" "029_4_C_day5" "081_3_S_day5"
[218] "034_3_C_day5" "002_2_C_day5" "074_2_C_day5" "033_1_C_day5" "006_1_S_day5" "079_1_S_day5" "033_1_S_day5"
[225] "035_1_C_day5" "035_4_S_day5" "011_2_S_day5" "029_2_S_day5" "005_1_S_day5" "001_4_C_day5" "028_2_C_day5"
[232] "101_3_C_day5" "013_1_S_day5" "006_2_S_day5" "096_2_C_day5" "040_2_S_day5" "098_2_C_day5" "076_4_C_day5"
[239] "102_2_C_day5" "012_1_C_day5" "001_3_S_day5" "078_2_S_day5" "012_4_C_day5" "002_3_S_day5" "076_2_S_day5"
[246] "032_2_S_day5" "096_3_S_day5" "032_1_S_day5" "101_4_C_day5" "002_4_S_day5" "101_4_S_day5" "059_1_S_day5"
[253] "036_1_S_day5" "079_2_C_day5" "008_2_C_day5" "098_4_C_day5" "095_2_C_day5" "011_1_S_day5" "036_1_C_day5"
[260] "094_1_S_day5" "081_4_C_day5" "034_3_S_day5" "031_2_S_day5" "106_1_C_day5" "036_2_S_day5" "030_3_S_day5"
[267] "080_4_C_day5" "040_4_S_day5" "040_3_C_day5" "076_1_S_day5" "032_3_C_day5" "029_2_C_day5" "101_1_C_day5"
[274] "078_2_C_day5" "105_2_S_day5" "037_1_S_day5" "055_1_C_day5" "003_1_C_day5" "038_4_S_day5" "002_3_C_day5"
[281] "096_3_C_day5" "034_2_S_day5" "102_1_C_day5" "102_1_S_day5" "076_4_S_day5" "032_4_C_day5" "098_2_S_day5"
[288] "104_1_C_day5" "076_3_S_day5" "075_2_S_day5" "058_1_S_day5" "102_3_S_day5" "095_2_S_day5" "099_3_S_day5"
[295] "009_4_S_day5" "104_4_S_day5" "031_3_C_day5" "081_2_C_day5" "006_1_C_day5" "074_2_S_day5" "009_3_C_day5"
[302] "009_4_C_day5" "075_1_S_day5" "028_4_S_day5" "008_4_C_day5" "058_3_C_day5" "029_1_S_day5" "078_1_S_day5"
[309] "103_2_C_day5" "106_2_S_day5" "076_2_C_day5" "095_3_C_day5" "008_4_S_day5" "003_1_S_day5" "074_1_S_day5"
[316] "096_1_S_day5" "104_4_C_day5" "059_4_S_day5" "105_2_C_day5" "013_3_S_day5" "011_2_C_day5" "074_3_S_day5"
[323] "074_1_C_day5" "034_2_C_day5" "095_1_C_day5" "033_3_C_day5" "005_2_C_day5" "104_2_C_day5" "005_4_S_day5"
[330] "028_2_S_day5" "292_3_S_day2" "212_1_S_day2" "307_2_C_day2" "282_3_C_day2" "199_2_S_day2" "278_1_S_day2"
[337] "301_1_C_day2" "287_1_S_day2" "213_2_C_day2" "214_1_C_day2" "286_2_C_day2" "194_1_S_day2" "212_1_C_day2"
[344] "285_2_S_day2" "284_2_S_day2" "285_3_S_day2" "278_1_C_day2" "196_2_S_day2" "201_2_S_day2" "307_1_S_day2"
[351] "279_1_S_day2" "293_3_S_day2" "280_1_S_day2" "194_3_S_day2" "299_2_C_day2" "207_1_S_day2" "298_3_C_day2"
[358] "282_1_C_day2" "285_3_C_day2" "307_3_C_day2" "200_1_S_day2" "211_3_S_day2" "308_3_C_day2" "298_3_S_day2"
[365] "308_2_C_day2" "287_1_C_day2" "301_1_S_day2" "299_1_C_day2" "278_3_C_day2" "283_2_S_day2" "306_2_S_day2"
[372] "280_1_C_day2" "220_1_C_day2" "213_2_S_day2" "287_3_S_day2" "194_3_C_day2" "194_1_C_day2" "282_2_S_day2"
[379] "290_3_C_day2" "196_2_C_day2" "198_2_C_day2" "292_2_C_day2" "299_3_S_day2" "195_2_S_day2" "289_2_S_day2"
[386] "282_2_C_day2" "299_1_S_day2" "300_3_C_day2" "298_2_C_day2" "288_3_C_day2" "307_1_C_day2" "201_3_C_day2"
[393] "291_3_C_day2" "277_2_S_day2" "220_1_S_day2" "2800_3_S_day2" "196_1_S_day2" "207_2_S_day2" "278_3_S_day2"
[400] "306_3_S_day2" "306_3_C_day2" "199_3_C_day2" "290_1_C_day2" "299_2_S_day2" "211_2_S_day2" "211_2_C_day2"
[407] "199_1_C_day2" "214_1_S_day2" "277_1_S_day2" "299_3_C_day2" "292_3_C_day2" "207_2_C_day2" "220_2_S_day2"
[414] "214_2_C_day2" "285_1_S_day2" "194_2_C_day2" "278_2_C_day2" "212_3_S_day2" "284_1_C_day2" "211_3_C_day2"
[421] "283_3_S_day2" "286_3_C_day2" "306_1_S_day2" "277_2_C_day2" "279_3_S_day2" "283_1_S_day2" "307_3_S_day2"
[428] "195_1_C_day2" "197_2_S_day2" "288_1_S_day2" "300_2_S_day2" "287_2_C_day2" "301_3_C_day2" "197_1_S_day2"
[435] "198_3_S_day2" "201_1_C_day2" "214_2_S_day2" "189_3_C_day2" "288_2_C_day2" "207_3_S_day2" "213_3_S_day2"
[442] "288_1_C_day2" "306_1_C_day2" "212_2_C_day2" "212_3_C_day2" "199_1_S_day2" "207_3_C_day2" "286_2_S_day2"
[449] "220_2_C_day2" "286_1_S_day2" "201_1_S_day2" "277_1_C_day2" "195_3_C_day2" "293_2_C_day2" "287_2_S_day2"
[456] "213_1_C_day2" "294_2_C_day2" "283_2_C_day2" "292_1_C_day2" "214_3_C_day2" "300_1_S_day2" "298_1_S_day2"
[463] "294_3_S_day2" "291_2_S_day2" "280_3_C_day2" "290_2_C_day2" "197_3_S_day2" "298_1_C_day2" "284_1_S_day2"
[470] "207_1_C_day2" "308_3_S_day2" "293_2_S_day2" "220_3_C_day2" "284_2_C_day2" "196_1_C_day2" "290_1_S_day2"
[477] "195_3_S_day2" "214_3_S_day2" "200_3_S_day2" "300_2_C_day2" "200_2_S_day2" "195_2_C_day2" "288_3_S_day2"
[484] "197_1_C_day2" "285_2_C_day2" "200_1_C_day2" "211_1_C_day2" "279_3_C_day2" "301_2_C_day2" "308_1_S_day2"
[491] "288_2_S_day2" "301_3_S_day2" "308_1_C_day2" "279_2_C_day2" "294_2_S_day2" "213_3_C_day2" "306_2_C_day2"
[498] "194_2_S_day2" "286_1_C_day2" "197_2_C_day2" "291_1_S_day2" "280_2_C_day2" "287_3_C_day2" "199_2_C_day2"
[505] "279_1_C_day2" "235_1_C_day2" "294_1_C_day2" "213_1_S_day2" "290_2_S_day2" "195_1_S_day2" "278_2_S_day2"
[512] "283_3_C_day2" "290_3_S_day2" "294_1_S_day2" "199_3_S_day2" "293_3_C_day2" "212_2_S_day2" "197_3_C_day2"
[519] "282_1_S_day2" "198_1_S_day2" "291_2_C_day2" "198_2_S_day2" "291_1_C_day2" "292_2_S_day2" "198_1_C_day2"
[526] "220_3_S_day2" "200_2_C_day2" "286_3_S_day2" "283_1_C_day2" "293_1_S_day2" "300_3_S_day2" "200_3_C_day2"
[533] "201_2_C_day2" "293_4_C_day2" "301_2_S_day2" "308_2_S_day2" "211_1_S_day2" "307_2_S_day2" "294_3_C_day2"
[540] "291_3_S_day2" "282_3_S_day2" "201_3_S_day2" "230_2_C_day1" "234_1_C_day1" "173_4_C_day1" "084_3_C_day1"
[547] "240_4_C_day1" "236_4_C_day1" "241_1_C_day1" "231_2_S_day1" "176_3_S_day1" "064_3_S_day1" "228_1_C_day1"
[554] "244_1_S_day1" "237_1_C_day1" "228_1_S_day1" "174_3_C_day1" "274_1_C_day1" "249_3_S_day1" "247_2_S_day1"
[561] "091_4_S_day1" "091_4_C_day1" "227_2_C_day1" "237_3_C_day1" "228_2_C_day1" "250_1_C_day1" "093_3_S_day1"
[568] "298_1_S_day1" "240_1_C_day1" "229_3_S_day1" "250_4_S_day1" "274_2_C_day1" "093_1_C_day1" "091_1_C_day1"
[575] "230_1_S_day1" "271_2_S_day1" "084_1_S_day1" "227_3_C_day1" "229_3_C_day1" "236_1_S_day1" "239_4_S_day1"
[582] "221_1_C_day1" "234_1_S_day1" "233_2_C_day1" "225_1_S_day1" "084_3_S_day1" "245_3_C_day1" "239_3_C_day1"
[589] "244_3_S_day1" "250_3_S_day1" "176_1_S_day1" "228_3_C_day1" "038_3_S_day1" "273_1_S_day1" "248_1_C_day1"
[596] "298_2_S_day1" "089_2_S_day1" "274_3_C_day1" "231_2_C_day1" "239_1_C_day1" "085_2_C_day1" "093_2_C_day1"
[603] "259_1_C_day1" "084_2_S_day1" "175_3_C_day1" "084_4_C_day1" "273_2_C_day1" "173_4_S_day1" "240_4_S_day1"
[610] "274_4_C_day1" "236_2_C_day1" "084_2_C_day1" "093_4_C_day1" "250_4_C_day1" "174_1_C_day1" "038_1_S_day1"
[617] "176_4_C_day1" "176_2_C_day1" "064_2_S_day1" "241_2_S_day1" "259_3_S_day1" "259_3_C_day1" "227_2_S_day1"
[624] "240_1_S_day1" "087_2_S_day1" "221_2_C_day1" "174_3_S_day1" "235_1_S_day1" "086_3_S_day1" "086_1_S_day1"
[631] "249_2_S_day1" "176_4_S_day1" "175_2_S_day1" "236_3_C_day1" "092_2_S_day1" "222_2_C_day1" "225_2_C_day1"
[638] "222_1_S_day1" "274_4_S_day1" "090_1_C_day1" "236_4_S_day1" "248_1_S_day1" "298_3_C_day1" "064_3_C_day1"
[645] "175_1_C_day1" "093_1_S_day1" "173_2_S_day1" "230_3_C_day1" "273_1_C_day1" "247_1_C_day1" "237_2_C_day1"
[652] "239_2_C_day1" "237_4_C_day1" "271_1_S_day1" "239_3_S_day1" "087_4_S_day1" "233_1_S_day1" "174_4_C_day1"
[659] "174_4_S_day1" "087_1_C_day1" "092_4_S_day1" "273_2_S_day1" "245_2_S_day1" "245_1_C_day1" "091_1_S_day1"
[666] "233_2_S_day1" "241_1_S_day1" "244_1_C_day1" "087_2_C_day1" "064_1_S_day1" "248_3_C_day1" "273_3_S_day1"
[673] "250_2_C_day1" "273_4_C_day1" "222_1_C_day1" "248_2_S_day1" "093_4_S_day1" "271_3_S_day1" "237_1_S_day1"
[680] "248_3_S_day1" "091_2_C_day1" "175_4_S_day1" "088_2_C_day1" "086_2_S_day1" "237_4_S_day1" "089_1_S_day1"
[687] "250_2_S_day1" "091_3_C_day1" "038_2_C_day1" "174_2_C_day1" "234_3_C_day1" "233_4_C_day1" "227_1_S_day1"
[694] "237_2_S_day1" "173_3_S_day1" "249_1_C_day1" "229_2_S_day1" "222_2_S_day1" "239_4_C_day1" "233_1_C_day1"
[701] "247_3_S_day1" "088_1_C_day1" "234_2_C_day1" "092_4_C_day1" "240_2_S_day1" "173_1_C_day1" "221_1_S_day1"
[708] "084_1_C_day1" "233_3_S_day1" "247_4_S_day1" "236_2_S_day1" "174_1_S_day1" "236_3_S_day1" "245_3_S_day1"
[715] "245_1_S_day1" "229_4_C_day1" "259_1_S_day1" "175_1_S_day1" "245_4_S_day1" "064_1_C_day1" "244_2_S_day1"
[722] "227_1_C_day1" "176_1_C_day1" "229_4_S_day1" "090_1_S_day1" "090_3_S_day1" "087_1_S_day1" "235_2_S_day1"
[729] "090_3_C_day1" "085_2_S_day1" "175_4_C_day1" "230_3_S_day1" "085_3_S_day1" "173_1_S_day1" "086_1_C_day1"
[736] "228_2_S_day1" "273_3_C_day1" "231_3_C_day1" "091_2_S_day1" "176_2_S_day1" "271_1_C_day1" "228_3_S_day1"
[743] "239_1_S_day1" "230_2_S_day1" "173_2_C_day1" "093_2_S_day1" "240_3_C_day1" "092_1_C_day1" "244_3_C_day1"
[750] "092_3_C_day1" "240_2_C_day1" "298_1_C_day1" "274_1_S_day1" "173_3_C_day1" "225_1_C_day1" "234_2_S_day1"
[757] "089_3_C_day1" "247_1_S_day1" "241_2_C_day1" "247_3_C_day1" "259_2_S_day1" "089_1_C_day1" "038_2_S_day1"
[764] "085_1_S_day1" "234_3_S_day1" "247_2_C_day1" "228_4_C_day1" "086_3_C_day1" "248_4_C_day1" "038_3_C_day1"
[771] "230_1_C_day1" "233_3_C_day1" "271_4_S_day1" "175_2_C_day1" "249_2_C_day1" "225_2_S_day1" "087_4_C_day1"
[778] "090_2_C_day1" "249_3_C_day1" "084_4_S_day1" "250_1_S_day1" "237_3_S_day1" "233_4_S_day1" "231_1_S_day1"
[785] "175_3_S_day1" "271_2_C_day1" "092_1_S_day1" "038_1_C_day1" "064_2_C_day1" "088_1_S_day1" "088_3_C_day1"
[792] "092_2_C_day1" "090_2_S_day1" "231_1_C_day1" "089_2_C_day1" "091_3_S_day1" "092_3_S_day1" "229_1_S_day1"
[799] "093_3_C_day1" "239_2_S_day1" "087_3_C_day1" "250_3_C_day1" "229_1_C_day1" "248_2_C_day1" "273_4_S_day1"
[806] "229_2_C_day1" "235_1_C_day1" "244_2_C_day1" "085_3_C_day1" "271_4_C_day1" "089_4_S_day1" "259_2_C_day1"
[813] "274_2_S_day1" "087_3_S_day1" "274_3_S_day1" "241_3_S_day1" "245_2_C_day1" "086_2_C_day1" "088_3_S_day1"
[820] "228_4_S_day1" "231_3_S_day1" "247_4_C_day1" "176_3_C_day1" "240_3_S_day1" "248_4_S_day1" "241_3_C_day1"
[827] "174_2_S_day1" "085_1_C_day1" "089_4_C_day1" "298_2_C_day1" "298_3_S_day1" "227_3_S_day1" "271_3_C_day1"
[834] "249_1_S_day1" "236_1_C_day1" "089_3_S_day1" "198_1_S_day5" "291_2_C_day5" "282_1_S_day5" "298_2_C_day5"
[841] "292_2_S_day5" "289_2_S_day5" "198_2_S_day5" "291_1_C_day5" "198_1_C_day5" "200_2_C_day5" "300_3_S_day5"
[848] "200_3_C_day5" "283_1_C_day5" "293_1_S_day5" "293_4_C_day5" "211_1_S_day5" "307_2_S_day5" "298_1_S_day5"
[855] "301_2_S_day5" "308_2_S_day5" "282_3_S_day5" "201_3_S_day5" "294_3_C_day5" "291_3_S_day5" "279_2_C_day5"
[862] "294_2_S_day5" "301_3_S_day5" "308_1_C_day5" "286_1_C_day5" "197_2_C_day5" "213_3_C_day5" "306_2_C_day5"
[869] "194_2_S_day5" "291_1_S_day5" "280_2_C_day5" "279_1_C_day5" "235_1_C_day5" "287_3_C_day5" "199_2_C_day5"
[876] "213_1_S_day5" "290_2_S_day5" "195_1_S_day5" "294_1_C_day5" "290_3_S_day5" "294_1_S_day5" "278_2_S_day5"
[883] "283_3_C_day5" "212_2_S_day5" "197_3_C_day5" "199_3_S_day5" "293_3_C_day5" "291_2_S_day5" "280_3_C_day5"
[890] "298_1_C_day5" "290_2_C_day5" "197_3_S_day5" "220_3_C_day5" "284_2_C_day5" "207_1_C_day5" "308_3_S_day5"
[897] "293_2_S_day5" "214_3_S_day5" "200_3_S_day5" "290_1_S_day5" "212_2_C_day5" "300_2_C_day5" "200_2_S_day5"
[904] "200_1_C_day5" "211_1_C_day5" "197_1_C_day5" "285_2_C_day5" "301_2_C_day5" "308_1_S_day5" "279_3_C_day5"
[911] "288_2_C_day5" "201_1_C_day5" "214_2_S_day5" "189_3_C_day5" "288_1_C_day5" "306_1_C_day5" "207_3_S_day5"
[918] "213_3_S_day5" "199_1_S_day5" "207_3_C_day5" "307_3_S_day5" "212_3_C_day5" "286_1_S_day5" "201_1_S_day5"
[925] "220_2_C_day5" "213_1_C_day5" "294_2_C_day5" "277_1_C_day5" "293_2_C_day5" "214_3_C_day5" "300_1_S_day5"
[932] "283_2_C_day5" "292_1_C_day5" "294_3_S_day5" "207_2_C_day5" "299_3_C_day5" "301_1_C_day5" "292_3_C_day5"
[939] "194_2_C_day5" "278_2_C_day5" "214_2_C_day5" "285_1_S_day5" "212_3_S_day5" "284_1_C_day5" "286_3_C_day5"
[946] "306_1_S_day5" "211_3_C_day5" "283_3_S_day5" "279_3_S_day5" "283_1_S_day5" "192_3_C_day5" "277_2_C_day5"
[953] "300_2_S_day5" "195_1_C_day5" "197_2_S_day5" "197_1_S_day5" "198_3_S_day5" "287_2_C_day5" "301_3_C_day5"
[960] "282_2_C_day5" "288_3_C_day5" "299_1_S_day5" "300_3_C_day5" "277_2_S_day5" "307_1_C_day5" "201_3_C_day5"
[967] "291_3_C_day5" "196_1_S_day5" "207_2_S_day5" "220_1_S_day5" "2800_3_S_day5" "278_3_S_day5" "306_3_S_day5"
[974] "290_1_C_day5" "306_3_C_day5" "199_3_C_day5" "199_1_C_day5" "214_1_S_day5" "277_1_S_day5" "211_2_S_day5"
[981] "211_2_C_day5" "285_3_C_day5" "207_1_S_day5" "298_3_C_day5" "282_1_C_day5" "211_3_S_day5" "308_3_C_day5"
[988] "307_3_C_day5" "200_1_S_day5" "287_1_C_day5" "301_1_S_day5" "298_3_S_day5" "308_2_C_day5" "283_2_S_day5"
[995] "299_1_C_day5" "278_3_C_day5" "213_2_S_day5" "287_3_S_day5" "280_1_C_day5" "220_1_C_day5"
[ reached getOption("max.print") -- omitted 4101 entries ]Pareto_all$arbor.name <- gsub(" ", "", Pareto_all$arbor.name)
meta$arbor.name <- gsub(" ", "", meta$arbor.name)
Pareto_meta <- merge(Pareto_all, meta, by="arbor.name")
Pareto_metadim(Pareto_meta)[1] 4617 13Let’s clean up this pareto_meta file:
colnames(Pareto_meta) [1] "arbor.name" "pareto.front.distance"
[3] "pareto.front.location" "pareto.front.scaling.distance"
[5] "pareto.front.scaling.location087_1_C_day4" "X1.072667"
[7] "X0.830000" "experiment"
[9] "day" "Picture.."
[11] "genotype" "replicate"
[13] "condition" Pareto <- Pareto_meta[,c(8:13, 1:5)]
dim(Pareto)[1] 4617 11colnames(Pareto)[11] <- "pareto.front.scaling.location"
ParetoNow - let’s try and connect it to the MR_all3 file:
all_MR3unique(all_MR3$genotype) [1] " 189" " 292" " 213" " 212" " 307" " 282" " 298" " 301" " 286" " 214" " 194" " 294" " 285" " 293" " 284" " 278"
[17] " 300" " 201" " 279" " 280" " 299" " 200" " 211" " 308" " 197" " 287" " 196" " 220" " 207" " 290" " 198" " 195"
[33] " 289" " 288" " 291" " 199" " 277" " 283" " 306" " 235" " 192" " 147" " 252" " 166" " 269" " 064" " 253" " 193"
[49] " 146" " 141" " 251" " 139" " 169" " 258" " 144" " 138" " 061" " 065" " 170" " 142" " 140" " 168" " 272" " 165"
[65] " 255" " 260" " 264" " 262" " 261" " 167" " 060" " 066" " 136" " 256" " 143" " 171" " 082" " 083" " 191" " 259"
[81] " 062" " 172" " 063" " 270" " 268" " 267" " 234" " 245" " 173" " 236" " 084" " 229" " 241" " 231" " 227" " 176"
[97] " 087" " 091" " 090" " 085" " 228" " 230" " 175" " 250" " 222" " 239" " 233" " 274" " 088" " 271" " 240" " 092"
[113] " 089" " 247" " 038" " 248" " 086" " 273" " 244" " 093" " 174" " 249" " 237" " 221" " 225" " 103" " 028" " 098"
[129] " 055" " 075" " 059" " 101" " 034" " 002" " 030" " 056" " 001" " 102" " 012" " 076" " 079" " 104" " 081" " 096"
[145] " 058" " 099" " 105" " 040" " 035" " 094" " 074" " 032" " 022" " 003" " 095" " 009" " 106" " 057" " 033" " 029"
[161] " 013" " 031" " 007" " 008" " 006" " 080" " 005" " 036" " 011" " 078" " 010" " 097" " 037"all_MR3$arbor.name <- paste(all_MR3$genotype, "_", all_MR3$rep, "_", all_MR3$cond, "_DAS", all_MR3$DAS, sep="")
head(all_MR3)
all_MR3$arbor.name <- gsub("DAS0", "day1", all_MR3$arbor.name)
all_MR3$arbor.name <- gsub("DAS1", "day2", all_MR3$arbor.name)
all_MR3$arbor.name <- gsub("DAS2", "day3", all_MR3$arbor.name)
all_MR3$arbor.name <- gsub("DAS3", "day4", all_MR3$arbor.name)
all_MR3$arbor.name <- gsub("DAS4", "day5", all_MR3$arbor.name)
all_MR3$arbor.name <- gsub(" ", "", all_MR3$arbor.name)
head(all_MR3)unique(all_MR3$arbor.name) %in% unique(Pareto$arbor.name) [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[19] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[55] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[73] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[109] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[127] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[145] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[163] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[199] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[217] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[235] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[307] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[325] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[343] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[379] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[397] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[415] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[433] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[469] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[487] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[505] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[523] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[559] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[577] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[595] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
[613] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[631] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[649] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[667] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[685] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[703] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[721] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[739] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[775] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[793] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[811] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[829] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[847] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[865] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[883] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[901] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[919] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[937] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[955] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[973] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[991] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[ reached getOption("max.print") -- omitted 4297 entries ]all_MR4 <- merge(all_MR3, Pareto, by="arbor.name")
dim(all_MR4)[1] 4990 75all_MR4Let’s clean it up a little before going further:
Pareto through time:
Now we have all the data - let’s have a look at how that is looking through time:
all_MR5 <- na.omit(all_MR4)
all_MR5$DAS <- as.factor(all_MR5$DAS)
PFdist_lgraph <- ggplot(data=all_MR5, aes(x= DAS, y=pareto.front.distance, group = root_name, color = cond))
PFdist_lgraph <- PFdist_lgraph +geom_line(alpha = 0.1)
PFdistance_lgraph <- PFdistance_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
PFdistance_lgraph <- PFdistance_lgraph + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.PFdistance_lgraph <- PFdistance_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
PFdistance_lgraph <- PFdistance_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Individual accessions")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.PFdistance_lgraph <- PFdistance_lgraph + ylab("Pareto Front Distance (a.u.)") + xlab("Days After Stress") + theme(legend.position='none')
PFdistance_lgraph
PFscdist_lgraph <- ggplot(data=all_MR5, aes(x= DAS, y=pareto.front.scaling.distance, group = root_name, color = cond))
PFscdist_lgraph <- PFscdist_lgraph +geom_line(alpha = 0.1)
PFscdist_lgraph <- PFscdist_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
PFscdist_lgraph <- PFscdist_lgraph + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.PFscdist_lgraph <- PFscdist_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
PFscdist_lgraph <- PFscdist_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Individual accessions")
PFscdist_lgraph <- PFscdist_lgraph + ylab("Pareto Front Scaling Distance (a.u.)") + xlab("Days After Stress") + theme(legend.position='none')
PFscdist_lgraph
PFloc_lgraph <- ggplot(data=all_MR5, aes(x= DAS, y=pareto.front.location, group = root_name, color = cond))
PFloc_lgraph <- PFloc_lgraph +geom_line(alpha = 0.1)
PFloc_lgraph <- PFloc_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
PFloc_lgraph <- PFloc_lgraph + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.PFloc_lgraph <- PFloc_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
PFloc_lgraph <- PFloc_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Individual accessions")
PFloc_lgraph <- PFloc_lgraph + ylab("Pareto Front Location (a.u.)") + xlab("Days After Stress") + theme(legend.position='none')
PFloc_lgraph
PFscloc_lgraph <- ggplot(data=all_MR5, aes(x= DAS, y=pareto.front.scaling.location, group = root_name, color = cond))
PFscloc_lgraph <- PFscloc_lgraph +geom_line(alpha = 0.1)
PFscloc_lgraph <- PFscloc_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
PFscloc_lgraph <- PFscloc_lgraph + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.PFscloc_lgraph <- PFscloc_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
PFscloc_lgraph <- PFscloc_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Individual accessions")
PFscloc_lgraph <- PFscloc_lgraph + ylab("Pareto Front Scaling Location (a.u.)") + xlab("Days After Stress") + theme(legend.position='none')
PFscloc_lgraph
ok - now let’s have a look what is the reproducibility of the pareto front location within the genotype:
interesting <- subset(all_MR5, all_MR5$genotype == " 248")
interesting
M248_PFScloc <- ggplot(data=interesting, aes(x= DAS, y=pareto.front.scaling.location, group = root_name, color = cond))
M248_PFScloc <- M248_PFScloc +geom_line(alpha = 0.2)
M248_PFScloc <- M248_PFScloc + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
M248_PFScloc <- M248_PFScloc + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.M248_PFScloc <- M248_PFScloc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
M248_PFScloc <- M248_PFScloc + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("M248")
M248_PFScloc <- M248_PFScloc + ylab("Pareto Front Scaling Location") + xlab("Days After Stress") + theme(legend.position='none')
M248_PFScloc
Let’s save this file and explore the heritability of these traits in MVApp:
write.csv(all_MR5, "Big01to04_incl.PF.csv", row.names = FALSE)The heritabiity of all the traits is pretty decent overall - between 0.4 and 0.7 in most cases.
Let’s see how the Pareto Front Scaling Location is correlated with LR traits:
all_MR5$DAS <- as.factor(all_MR5$DAS)
PFsc.loc_vs_TRS <- ggscatter(all_MR5, x = "pareto.front.scaling.location", y = "TRS", color = "cond", size = 0.3,
rug = TRUE, facet.by = "DAS") + stat_cor(method = "pearson")
PFsc.loc_vs_TRS
PFsc.loc_vs_LRno <- ggscatter(all_MR5, x = "pareto.front.scaling.location", y = "LRno", color = "cond", size = 0.3,
rug = TRUE, facet.by = "DAS") + stat_cor(method = "pearson")
PFsc.loc_vs_LRno
PFsc.loc_vs_aLRL <- ggscatter(all_MR5, x = "pareto.front.scaling.location", y = "aLRL", color = "cond", size = 0.3,
rug = TRUE, facet.by = "DAS") + stat_cor(method = "pearson")
PFsc.loc_vs_aLRL
PFsc.loc_vs_CoG <- ggscatter(all_MR5, x = "pareto.front.scaling.location", y = "CoG", color = "cond", size = 0.3,
rug = TRUE, facet.by = "DAS") + stat_cor(method = "pearson")
PFsc.loc_vs_CoG
actually - CoG should be calculated as perc. or MR - I wonder why I didnt do that before :)
colnames(all_MR5) [1] "arbor.name" "image" "root_name"
[4] "root" "experiment" "DAS"
[7] "genotype" "rep" "cond"
[10] "MRL" "Apical" "Branched"
[13] "Basal" "Apical_perc" "Branched_perc"
[16] "Basal_perc" "LR_no_10_100" "LR_no_20_100"
[19] "LR_no_30_100" "LR_no_40_100" "LR_no_50_100"
[22] "LR_no_60_100" "LR_no_70_100" "LR_no_80_100"
[25] "LR_no_90_100" "LR_no_100_100" "LRL_10_100"
[28] "LRL_20_100" "LRL_30_100" "LRL_40_100"
[31] "LRL_50_100" "LRL_60_100" "LRL_70_100"
[34] "LRL_80_100" "LRL_90_100" "LRL_100_100"
[37] "CoG" "LRL.dec" "LRL.dec.R2"
[40] "LRL" "LRno" "aLRL"
[43] "TRS" "MRL.p.TRS" "aLRL.p.TRS"
[46] "LRL_10_perc" "LRL_20_perc" "LRL_30_perc"
[49] "LRL_40_perc" "LRL_50_perc" "LRL_60_perc"
[52] "LRL_70_perc" "LRL_80_perc" "LRL_90_perc"
[55] "LRL_100_perc" "LRno_10_perc" "LRno_20_perc"
[58] "LRno_30_perc" "LRno_40_perc" "LRno_50_perc"
[61] "LRno_60_perc" "LRno_70_perc" "LRno_80_perc"
[64] "LRno_90_perc" "LRno_100_perc" "pareto.front.distance"
[67] "pareto.front.location" "pareto.front.scaling.distance" "pareto.front.scaling.location"
[70] "CoG.per" all_MR5$CoG.per <- all_MR5$CoG / all_MR5$MRL
CoGperc_lgraph <- ggplot(data=all_MR5, aes(x= DAS, y=CoG.per, group = root_name, color = cond))
CoGperc_lgraph <- CoGperc_lgraph +geom_line(alpha = 0.1)
CoGperc_lgraph <- CoGperc_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
CoGperc_lgraph <- CoGperc_lgraph + stat_summary(fun.y=mean, aes(group= cond), size=0.7, geom="line", linetype = "dashed")`fun.y` is deprecated. Use `fun` instead.CoGperc_lgraph <- CoGperc_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
CoGperc_lgraph <- CoGperc_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Individual accessions")
CoGperc_lgraph <- CoGperc_lgraph + ylab("Fraction of the Main Root") + xlab("Days After Stress") + theme(legend.position='none')
CoGperc_lgraph
PFsc.loc_vs_CoG.perc <- ggscatter(all_MR5, x = "pareto.front.scaling.location", y = "CoG.per", color = "cond", size = 0.3,
rug = TRUE, facet.by = "DAS") + stat_cor(method = "pearson")
PFsc.loc_vs_CoG.perc
Also - I would like to have a look at how this scaling.location looks across the accessions within one day and between conditions - lets do DAS4
DAS4 <- subset(all_MR5, all_MR5$DAS == 4)
grand.means <- aggregate(pareto.front.scaling.location ~ cond, data = DAS4, FUN = mean)
grand.means
unique(DAS4$genotype) [1] " 001" " 002" " 003" " 005" " 006" " 007" " 008" " 009" " 010" " 011" " 012" " 013" " 022" " 028" " 029" " 030"
[17] " 031" " 032" " 033" " 034" " 035" " 036" " 037" " 038" " 040" " 055" " 056" " 057" " 058" " 059" " 060" " 061"
[33] " 062" " 063" " 064" " 065" " 066" " 074" " 075" " 076" " 078" " 079" " 080" " 081" " 082" " 083" " 084" " 085"
[49] " 086" " 087" " 088" " 089" " 090" " 091" " 092" " 093" " 094" " 095" " 096" " 097" " 098" " 099" " 101" " 102"
[65] " 103" " 104" " 105" " 106" " 136" " 138" " 139" " 140" " 141" " 142" " 143" " 144" " 146" " 147" " 165" " 166"
[81] " 167" " 168" " 169" " 170" " 171" " 172" " 173" " 174" " 175" " 176" " 189" " 191" " 192" " 193" " 194" " 195"
[97] " 196" " 197" " 198" " 199" " 200" " 201" " 207" " 211" " 212" " 213" " 214" " 220" " 221" " 222" " 225" " 227"
[113] " 228" " 229" " 230" " 231" " 233" " 234" " 235" " 236" " 237" " 239" " 240" " 241" " 244" " 245" " 247" " 248"
[129] " 249" " 250" " 251" " 252" " 253" " 255" " 256" " 258" " 259" " 260" " 261" " 262" " 264" " 267" " 268" " 269"
[145] " 270" " 271" " 272" " 273" " 274" " 277" " 278" " 279" " 280" " 282" " 283" " 284" " 285" " 286" " 287" " 288"
[161] " 289" " 290" " 291" " 292" " 293" " 294" " 298" " 299" " 300" " 301" " 306" " 307" " 308"PF.sc.loc_p_geno <- ggerrorplot(DAS4, y = "pareto.front.scaling.location", x = "genotype", fill="genotype",
color="genotype", facet.by = c("cond"), ncol=1, desc_stat = "mean_sd", add = "jitter",
add.params = list(color = "darkgray"), xlab="Genotype",
ylab="Pareto Front Scaling Location (a.u.)")
PF.sc.loc_p_geno <- PF.sc.loc_p_geno + geom_hline(
data = grand.means, aes(yintercept = pareto.front.scaling.location),
linetype = 2,
group = "cond")
PF.sc.loc_p_geno <- PF.sc.loc_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = ".all.",
label = "p.signif", hide.ns = T)
PF.sc.loc_p_geno
Let’s calculate the change in the pareto front scaling location relative to control conditions per accession as well.
---
title: "Analysis of ALL Pimp BIG experiment"
output: html_notebook
---

# file preparation

PimpBig2 experiment analysis - loading the data   

```{r}
setwd("/Users/magdalena/Dropbox/DataAndAnalysis/Tomato/PimpiBIG csv raw files/")
list.files()
Big1_D0D1 <- read.csv("pimpi_Big1_D0_D1.csv")
Big1_D2D3 <- read.csv("pimpi_Big1_D2_D3.csv")
Big1_D4D5 <- read.csv("pimpi_Big1_D4_D5.csv")

Big2_D0D1 <- read.csv("pimpi_Big2_D0_D1.csv")
Big2_D2D3 <- read.csv("pimpi_Big2_D2_D3.csv")
Big2_D4D5 <- read.csv("pimpi_Big2_D4_D5.csv")

Big3_D0D1 <- read.csv("pimpi_Big3_D0_D1.csv")
Big3_D2D3 <- read.csv("pimpi_Big3_D2_D3.csv")
Big3_D4D5 <- read.csv("pimpi_Big3_D4_D5.csv")

Big4_D0D1 <- read.csv("pimpi_Big4_D0_D1.csv")
Big4_D0_FU <- read.csv("pimpi_Big4_D0_FU.csv")
Big4_D2D3 <- read.csv("pimpi_Big4_D2_D3.csv")
Big4_D4D5 <- read.csv("pimpi_Big4_D4_D5.csv")

head(Big4_D0D1)
head(Big4_D0_FU)
# Need to add this because otherwise we have images that are called exactly the same and it is causing problems later on 
Big4_D0_FU$image <- gsub(".rsml", "_FU.rsml", Big4_D0_FU$image)
head(Big4_D0_FU)
head(Big4_D2D3)
head(Big4_D4D5)
```

Let's fuse all the files into one big file per experiment

```{r}
Big1 <- rbind(Big1_D0D1, Big1_D2D3)
Big1 <- rbind(Big1, Big1_D4D5)

Big2 <- rbind(Big2_D0D1, Big2_D2D3)
Big2 <- rbind(Big2, Big2_D4D5)

Big3 <- rbind(Big3_D0D1, Big3_D2D3)
Big3 <- rbind(Big3, Big3_D4D5)

Big4 <- rbind(Big4_D0D1, Big4_D0_FU)
Big4 <- rbind(Big4, Big4_D2D3)
Big4 <- rbind(Big4, Big4_D4D5)
```

Let's make sure we have a collumn indicating the experimental batch as well:

```{r}
Big1$experiment <- "Big01"
Big2$experiment <- "Big02"
Big3$experiment <- "Big03"
Big4$experiment <- "Big04"
```

There is an issue with the images from Big02 also being in Big04 - so we have to make sure that we have only images from particular experiment in there. So - we need to make a collumn for everyone with "date" to make sure we only have the following days

Big 1 experiment:
```{r}
Big1$info <- strsplit(Big1$image, "_")
dim(Big1)
Big1$date <- "none"
for(i in 1:8555){
  Big1$date[i] <- sapply(Big1$info[i], function(x){
  x[4]})
}
unique(Big1$date)
```

Big 2 experiment:
```{r}
Big2$info <- strsplit(Big2$image, "_")
dim(Big2)
Big2$date <- "none"
for(i in 1:13695){
  Big2$date[i] <- sapply(Big2$info[i], function(x){
  x[4]})
}
unique(Big2$date)
```

Big 3 experiment:
```{r}
Big3$info <- strsplit(Big3$image, "_")
dim(Big3)
Big3$date <- "none"
for(i in 1:13232){
  Big3$date[i] <- sapply(Big3$info[i], function(x){
  x[4]})
}
unique(Big3$date)
```

Big 4 experiment:
```{r}
Big4$info <- strsplit(Big4$image, "_")
dim(Big4)
Big4$date <- "none"
for(i in 1:13346){
  Big4$date[i] <- sapply(Big4$info[i], function(x){
  x[4]})
}
unique(Big4$date)
```

Now we can fuse all the data into one:

```{r}
all_data <- rbind(Big1, Big2)
all_data <- rbind(all_data, Big3)
all_data <- rbind(all_data, Big4)
dim(all_data)
```

Then - we need to check how many "Root" values we have per unique image and how many "Lateral root" values we have. This will save us heaps of headache in the future

In total we have X many images
```{r}
length(unique(all_data$image))
```

and we have X many Main Roots - which *should* be the same as image number...

```{r}
onlyMr <- subset(all_data, all_data$root_order == 0)
dim(onlyMr)
```

... but it isn't - so let's figure which images have more MR per image than 1 and remove these specific values (or decide which one to remove :)

We need to make a file where:
Col. A > image 
Col. B > # Root
Col. C > # Lateral Root

```{r}
names <- c(text="image", "MR.no", "LR.no")
check_table <- data.frame()

for (k in names) check_table[[k]] <- as.character()
```

Then - we will subset the entire dataset for one image and calculate how many MR and LR are there and integrate it into the table

```{r}
i=1

uni <- subset(all_data, all_data$image == unique(all_data$image)[i])
uni
MR <- subset(uni, uni$root_order == 0)
LR <- subset(uni, uni$root_order == 1)

check_table[i,1] <- as.character(unique(uni$image))
check_table[i,2] <- dim(MR)[1]
check_table[i,3] <- dim(LR)[1]
check_table
```
Cool - now let's loop it for all the images:
```{r}

for (i in 2:5521) {
  uni <- subset(all_data, all_data$image == unique(all_data$image)[i])
  uni
  MR <- subset(uni, uni$root_order == 0)
  LR <- subset(uni, uni$root_order == 1)
  
  check_table[i,1] <- as.character(unique(uni$image))
  check_table[i,2] <- dim(MR)[1]
  check_table[i,3] <- dim(LR)[1]
}

check_table
```

Cool - now let's fish out all the images where we have multiple MR:

```{r}
suspicious <- subset(check_table, check_table$MR.no > 1)
dim(suspicious)
suspicious
```

Let's have a closer look at these suspicious images:

```{r}
closer_look <- subset(all_data, all_data$image == suspicious$image[100])
closer_look2 <- subset(closer_look, closer_look$root_order == 0)
closer_look2
```

It looks like these double roots are just duplications - let's see what happens when we remove exact duplicates using tidyverse package

```{r}
library(tidyverse)
all_data2 <- all_data %>% distinct()
length(unique((all_data2$image)))

onlyMr2 <- subset(all_data2, all_data2$root_order == 0)
dim(onlyMr2)
```

cool - so now we have majority of the images where the MR is simply duplicated - so let's repeat the exercise from above again to identify what other images we have left over which have two MR:

```{r}
names <- c(text="image", "MR.no", "LR.no")
check_table2 <- data.frame()

for (k in names) check_table2[[k]] <- as.character()

for (i in 1:5521) {
  uni <- subset(all_data2, all_data2$image == unique(all_data2$image)[i])
  MR <- subset(uni, uni$root_order == 0)
  LR <- subset(uni, uni$root_order == 1)
  
  check_table2[i,1] <- as.character(unique(uni$image))
  check_table2[i,2] <- dim(MR)[1]
  check_table2[i,3] <- dim(LR)[1]
}

suspicious <- subset(check_table2, check_table2$MR.no > 1)
suspicious
```

Let's have a closer look at these images and their MRs:
```{r}
closer_look <- subset(all_data2, all_data2$image == suspicious$image[1])
closer_look1 <- subset(closer_look, closer_look$root_order == 0)
closer_look1

closer_look <- subset(all_data2, all_data2$image == suspicious$image[22])
closer_look2 <- subset(closer_look, closer_look$root_order == 0)
closer_look2
```

OK - so clearly - we  have something that wasnt classified as LR properly (as in case of closer_look1).

We can recognize the "mistake MR" by saying that the name would start with root_ but let's see if this is the case for many of them:

```{r}
onlyMr <- subset(all_data2, all_data2$root_order == 0)
unique(onlyMr$root_name)
```

ok - so then we have to split the root name into individual tiers and select MR which start with "root_":

```{r}
all_data2$root_info <- strsplit(all_data2$root_name, "_")
dim(all_data2)

for(i in 1:41317){
  all_data2$genotype[i] <- sapply(all_data2$root_info[i], function(x){
  x[1]})
  all_data2$rep[i] <- sapply(all_data2$root_info[i], function(x){
  x[2]})
  all_data2$cond[i] <- sapply(all_data2$root_info[i], function(x){
  x[3]})
}

head(all_data2)
```

Now we have to subset for Mr and look at their individual genotype information

```{r}
onlyMr <- subset(all_data2, all_data2$root_order == 0)
dim(onlyMr)
unique(onlyMr$genotype)
onlyMr$genotype <- gsub(" ", "", onlyMr$genotype)
unique(onlyMr$genotype)
onlyMr2 <- subset(onlyMr, onlyMr$genotype != "root")
dim(onlyMr2)
unique(onlyMr2$genotype)
```

Now - lets get rid of these bad MR from the original all_data2 file:

```{r}
badMr <- subset(onlyMr, onlyMr$genotype == "root")
badMr
wrong_MR <- badMr$root
wrong_MR

all_data2_single_MR <- all_data2[!(all_data2$root %in% wrong_MR),]
dim(all_data2_single_MR)
dim(all_data2)

length(unique((all_data2_single_MR$image)))

onlyMr3 <- subset(all_data2_single_MR, all_data2_single_MR$root_order == 0)
dim(onlyMr3)
```

So we still have 7 images that supposedly contain more than 2 roots

Let's look at these images:

```{r}
names <- c(text="image", "MR.no", "LR.no")
check_table2 <- data.frame()

for (k in names) check_table2[[k]] <- as.character()

for (i in 1:5520) {
  uni <- subset(all_data2_single_MR, all_data2_single_MR$image == unique(all_data2_single_MR$image)[i])
  MR <- subset(uni, uni$root_order == 0)
  LR <- subset(uni, uni$root_order == 1)
  
  check_table2[i,1] <- as.character(unique(uni$image))
  check_table2[i,2] <- dim(MR)[1]
  check_table2[i,3] <- dim(LR)[1]
}

suspicious <- subset(check_table2, check_table2$MR.no > 1)
suspicious
```

```{r}
closer_look <- subset(all_data2_single_MR, all_data2_single_MR$image == suspicious$image[1])
closer_look1 <- subset(closer_look, closer_look$root_order == 0)
closer_look1

closer_look <- subset(all_data2_single_MR, all_data2_single_MR$image == suspicious$image[2])
closer_look2 <- subset(closer_look, closer_look$root_order == 0)
closer_look2
```

from the look of it - it seems we have two cases where we have exported twice the exact same root but one longer than the other (I presume longer is correct one). Problem is that their root and image are both identical. 

After inspecting the images - it seems that:
_set1_day3_20191002_238.tiff has > 2 lateral roots 
_set1_day3_20191002_001 has > 3 lateral roots

so in both cases - the 2nd Main Root is better. I am affraid that we will also get some oddness with positions of lateral roots. So maybe let's inspect all the data belonging to these two images:

```{r}
odd_stuff01 <- subset(all_data2_single_MR, all_data2_single_MR$image == "_set1_day3_20191002_238.rsml")
odd_stuff02 <- subset(all_data2_single_MR, all_data2_single_MR$image == "_set1_day3_20191002_001.rsml")
odd_stuff01
odd_stuff02
```

ok - so based on this data - it seems like:
- the first 3 rows in odd_stuff01 are containing faulty data, and thus should be removed
- the first 4 rows in odd_stuff02 are containing faulty data, and thus should be removed

So let's remove these columns from individual odd_stuff files, remove the entire picture from the all_data2_single_MR, and then rbind() the odd_stuff together back into the all_data2_single_MR:

```{r}
odd_stuff01 <- odd_stuff01[4:13,]
odd_stuff01

odd_stuff02 <- odd_stuff02[5:14,]
odd_stuff02

bye <- c("_set1_day3_20191002_238.rsml", "_set1_day3_20191002_001.rsml")
all_data3 <- subset(all_data2_single_MR, !(all_data2_single_MR$image %in% bye))
all_data3 <- rbind(all_data3, odd_stuff01)
all_data3 <- rbind(all_data3, odd_stuff02)
```

Let's do a final check if we have NO other images that have two MR now:

```{r}
length(unique(all_data3$image))
names <- c(text="image", "MR.no", "LR.no")
check_table2 <- data.frame()

for (k in names) check_table2[[k]] <- as.character()

for (i in 1:5520) {
  uni <- subset(all_data3, all_data3$image == unique(all_data3$image)[i])
  MR <- subset(uni, uni$root_order == 0)
  LR <- subset(uni, uni$root_order == 1)
  
  check_table2[i,1] <- as.character(unique(uni$image))
  check_table2[i,2] <- dim(MR)[1]
  check_table2[i,3] <- dim(LR)[1]
}

suspicious <- subset(check_table2, check_table2$MR.no > 1)
suspicious
```

Great - no suspicious images anymore - so we can move on :)

# calculating desired traits:

Let's now get rid of all the traits like volume and diameter that we are not directly interested in:

```{r}
head(all_data3)
colnames(all_data3)
final <- all_data3[,c(1:3, 20, 22, 24:26, 4, 9, 11, 12,14,17,19)]
head(final)
```
then - we need to create a file that has all MR:

```{r}
only_MR <- subset(final, final$root_ontology == unique(final$root_ontology)[1])
colnames(only_MR)[9] <- "MRL"
head(only_MR)
```

and since we dont need to calculate any LR related phenotypes for the plants that dont have LR - we need to subset our MR file a bit further:

```{r}
no_LR_MR <- subset(only_MR, only_MR$n_child < 1)
only_MR <- subset(only_MR, only_MR$n_child > 0)
head(only_MR)
head(no_LR_MR)
```

Cool - there are now quite a lot of unneccessary information now - so let's get to bare necessities:

```{r}
colnames(only_MR)
only_MR <- only_MR[,c(1:9,13:15)]
no_LR_MR <- no_LR_MR[,c(1:9,13:15)]
dim(only_MR)
head(only_MR)
```
We have the same root ID occurring multiple times - as we are tracing individual plants accross the time and transferring the data from one image to another - therefore - let's check if it all adds up:

```{r}
all_MR <- unique(only_MR$root)
length(all_MR)
all_img <- unique(only_MR$image)
length(all_img)

length(all_img) / length(all_MR)

only_MR$image_id <- paste(only_MR$image, "_", only_MR$root_name, sep="")
head(only_MR)

all_img_id <- unique(only_MR$image_id)
length(all_img_id)
```

Almost - but we will deal with it later.

So we have in total 4460 unique images - we will have to examine the LR belonging to each MR in each image by extracting them based on the "date":

```{r}
MR_now <- all_img[4460]
MR_now

head(final)

super_temp <- subset(final, final$image %in% MR_now)
super_temp
```

Then - we need to create some info about the length of individual zones - for this we need info on MR_length:

```{r}
MR_length <- subset(only_MR, only_MR$image %in% MR_now)
MR_length <- MR_length$MRL
MR_length
```

Length of Apical zone is basically equal to position of first lateral root Branched zone length is the position of last LR - position of first LR Basal zone is the MR length - position of last LR thus:

```{r}
super_temp <- subset(super_temp, super_temp$root_ontology == unique(super_temp$root_ontology)[2])
super_temp
Apical <- min(super_temp$insertion_position)
Branched <- (max(super_temp$insertion_position) - Apical)
Basal <- MR_length - max(super_temp$insertion_position)
Apical
Branched
Basal
```

Another thing that I am interested in is the distribution of LRL across the MR and how quickly that decreases across the MRL

```{r}
super_temp
#super_temp$adj_position <- super_temp$insertion_position + 0.0000001
plot(super_temp$length ~ super_temp$insertion_position)
abline(lm(super_temp$length ~ super_temp$insertion_position))
model <- lm(super_temp$length ~ super_temp$insertion_position)
summary(model)
model$coefficients
LRL.dec <- model$coefficients[[2]]
LRL.dec.R2 <- summary(model)$r.squared
LRL.dec.R2
LRL.dec
```


Then - we can calculate the % of LR across the MR length - by dividing the MR into 4 or 10 parts

```{r}
super_temp$LR_in_10_perc <- 0
super_temp$LR_in_20_perc <- 0
super_temp$LR_in_30_perc <- 0
super_temp$LR_in_40_perc <- 0
super_temp$LR_in_50_perc <- 0
super_temp$LR_in_60_perc <- 0
super_temp$LR_in_70_perc <- 0
super_temp$LR_in_80_perc <- 0
super_temp$LR_in_90_perc <- 0
super_temp$LR_in_100_perc <- 0


for(e in 1:nrow(super_temp)){
   if (super_temp$insertion_position[e] < (MR_length/10)){
    super_temp$LR_in_10_perc[e] <- 1
   } else {super_temp$LR_in_10_perc[e] <- 0}
  if (super_temp$insertion_position[e] < 2*(MR_length/10) & super_temp$insertion_position[e] > (MR_length/10)){
    super_temp$LR_in_20_perc[e] <- 1
  } else {super_temp$LR_in_20_perc[e] <- 0}
  if (super_temp$insertion_position[e] < 3*(MR_length/10) & super_temp$insertion_position[e] > 2*(MR_length/10)){
    super_temp$LR_in_30_perc[e] <- 1
  } else {super_temp$LR_in_30_perc[e] <- 0}
  if (super_temp$insertion_position[e] < 4*(MR_length/10) & super_temp$insertion_position[e] > 3*(MR_length/10)){
    super_temp$LR_in_40_perc[e] <- 1
  } else {super_temp$LR_in_40_perc[e] <- 0}
  if (super_temp$insertion_position[e] < 5*(MR_length/10) & super_temp$insertion_position[e] > 4*(MR_length/10)){
    super_temp$LR_in_50_perc[e] <- 1
  } else {super_temp$LR_in_50_perc[e] <- 0}
  if (super_temp$insertion_position[e] < 6*(MR_length/10) & super_temp$insertion_position[e] > 5*(MR_length/10)){
    super_temp$LR_in_60_perc[e] <- 1
  } else {super_temp$LR_in_60_perc[e] <- 0}
  if (super_temp$insertion_position[e] < 7*(MR_length/10) & super_temp$insertion_position[e] > 6*(MR_length/10)){
    super_temp$LR_in_70_perc[e] <- 1
  } else {super_temp$LR_in_70_perc[e] <- 0}
  if (super_temp$insertion_position[e] < 8*(MR_length/10) & super_temp$insertion_position[e] > 7*(MR_length/10)){
    super_temp$LR_in_80_perc[e] <- 1
  } else {super_temp$LR_in_80_perc[e] <- 0}
  if (super_temp$insertion_position[e] < 9*(MR_length/10) & super_temp$insertion_position[e] > 8*(MR_length/10)){
    super_temp$LR_in_90_perc[e] <- 1
  } else {super_temp$LR_in_90_perc[e] <- 0}
  if (super_temp$insertion_position[e] < 10*(MR_length/10) & super_temp$insertion_position[e] > 9*(MR_length/10)){
    super_temp$LR_in_100_perc[e] <- 1
  } else {super_temp$LR_in_100_perc[e] <- 0}
}

super_temp
```

so after classifying each LR to its respective portion of MR, we can then summarize them and calculate total LR length in this fragment:

```{r}
super_temp$LRL_in_10_perc <- super_temp$length * super_temp$LR_in_10_perc
super_temp$LRL_in_20_perc <- super_temp$length * super_temp$LR_in_20_perc
super_temp$LRL_in_30_perc <- super_temp$length * super_temp$LR_in_30_perc
super_temp$LRL_in_40_perc <- super_temp$length * super_temp$LR_in_40_perc
super_temp$LRL_in_50_perc <- super_temp$length * super_temp$LR_in_50_perc
super_temp$LRL_in_60_perc <- super_temp$length * super_temp$LR_in_60_perc
super_temp$LRL_in_70_perc <- super_temp$length * super_temp$LR_in_70_perc
super_temp$LRL_in_80_perc <- super_temp$length * super_temp$LR_in_80_perc
super_temp$LRL_in_90_perc <- super_temp$length * super_temp$LR_in_90_perc
super_temp$LRL_in_100_perc <- super_temp$length * super_temp$LR_in_100_perc

LR_no_10_100 <- sum(super_temp$LR_in_10_perc)
LR_no_20_100 <- sum(super_temp$LR_in_20_perc)
LR_no_30_100 <- sum(super_temp$LR_in_30_perc)
LR_no_40_100 <- sum(super_temp$LR_in_40_perc)
LR_no_50_100 <- sum(super_temp$LR_in_50_perc)
LR_no_60_100 <- sum(super_temp$LR_in_60_perc)
LR_no_70_100 <- sum(super_temp$LR_in_70_perc)
LR_no_80_100 <- sum(super_temp$LR_in_80_perc)
LR_no_90_100 <- sum(super_temp$LR_in_90_perc)
LR_no_100_100 <- sum(super_temp$LR_in_100_perc)

LRL_10_100 <- sum(super_temp$LRL_in_10_perc)
LRL_20_100 <- sum(super_temp$LRL_in_20_perc)
LRL_30_100 <- sum(super_temp$LRL_in_30_perc)
LRL_40_100 <- sum(super_temp$LRL_in_40_perc)
LRL_50_100 <- sum(super_temp$LRL_in_50_perc)
LRL_60_100 <- sum(super_temp$LRL_in_60_perc)
LRL_70_100 <- sum(super_temp$LRL_in_70_perc)
LRL_80_100 <- sum(super_temp$LRL_in_80_perc)
LRL_90_100 <- sum(super_temp$LRL_in_90_perc)
LRL_100_100 <- sum(super_temp$LRL_in_100_perc)
```

OK - last but not least - we should calculate the center of gravity for LR:

```{r}
super_temp$momentum <- (super_temp$insertion_position * super_temp$length)
all_momentum <- sum(super_temp$momentum)
all_momentum
all_length <- sum(super_temp$length)
all_length
CoG <- (all_momentum / all_length)
CoG
```

Finally - let’s add all these values into our MR_only table

First we need to create needed columns:
```{r}
only_MR$Apical <- 0
only_MR$Branched <- 0
only_MR$Basal <- 0

only_MR$Apical_perc <- 0
only_MR$Branched_perc <- 0
only_MR$Basal_perc <- 0
only_MR$LR_no_10_100 <- 0
only_MR$LR_no_20_100 <- 0
only_MR$LR_no_30_100 <- 0
only_MR$LR_no_40_100 <- 0
only_MR$LR_no_50_100 <- 0
only_MR$LR_no_60_100 <- 0
only_MR$LR_no_70_100 <- 0
only_MR$LR_no_80_100 <- 0
only_MR$LR_no_90_100 <- 0
only_MR$LR_no_100_100 <- 0

only_MR$LRL_10_100 <- 0
only_MR$LRL_20_100 <- 0
only_MR$LRL_30_100 <- 0
only_MR$LRL_40_100 <- 0
only_MR$LRL_50_100 <- 0
only_MR$LRL_60_100 <- 0
only_MR$LRL_70_100 <- 0
only_MR$LRL_80_100 <- 0
only_MR$LRL_90_100 <- 0
only_MR$LRL_100_100 <- 0

only_MR$CoG <- 0
only_MR$LRL.dec <- 0
only_MR$LRL.dec.R2 <- 0
head(only_MR)
```

then add values to these columns:

```{r}
only_MR$Apical[1] <- Apical
only_MR$Branched[1] <- Branched
only_MR$Basal[1] <- Basal

only_MR$Apical_perc[1] <- (Apical / MR_length)
only_MR$Branched_perc[1] <- (Branched / MR_length)
only_MR$Basal_perc[1] <- (Basal / MR_length)
only_MR$LR_no_10_100[1] <- LR_no_10_100
only_MR$LR_no_20_100[1] <- LR_no_20_100
only_MR$LR_no_30_100[1] <- LR_no_30_100
only_MR$LR_no_40_100[1] <- LR_no_40_100
only_MR$LR_no_50_100[1] <- LR_no_50_100
only_MR$LR_no_60_100[1] <- LR_no_60_100
only_MR$LR_no_70_100[1] <- LR_no_70_100
only_MR$LR_no_80_100[1] <- LR_no_80_100
only_MR$LR_no_90_100[1] <- LR_no_90_100
only_MR$LR_no_100_100[1] <- LR_no_100_100

only_MR$LRL_10_100[1] <- LRL_10_100
only_MR$LRL_20_100[1] <- LRL_20_100
only_MR$LRL_30_100[1] <- LRL_30_100
only_MR$LRL_40_100[1] <- LRL_40_100
only_MR$LRL_50_100[1] <- LRL_50_100
only_MR$LRL_60_100[1] <- LRL_60_100
only_MR$LRL_70_100[1] <- LRL_70_100
only_MR$LRL_80_100[1] <- LRL_80_100
only_MR$LRL_90_100[1] <- LRL_90_100
only_MR$LRL_100_100[1] <- LRL_100_100

only_MR$CoG[1] <- CoG

only_MR$LRL.dec[1] <- LRL.dec
only_MR$LRL.dec.R2[1] <- LRL.dec.R2  

head(only_MR)
```
cool - looks good. Let's loop it:


```{r}
dim(final)
length(all_img)
```

```{r}
for(i in c(2:4460)){
  MR_now <- all_img[i]
  super_temp <- subset(final, final$image %in% MR_now)
  MR_length <- subset(only_MR, only_MR$image %in% MR_now)
  MR_length <- MR_length$MRL
  super_temp <- subset(super_temp, super_temp$root_ontology == unique(super_temp$root_ontology)[2])
  Apical <- min(super_temp$insertion_position)
  Branched <- (max(super_temp$insertion_position) - Apical)
  Basal <- MR_length - max(super_temp$insertion_position)
  
  super_temp$LR_in_10_perc <- 0
  super_temp$LR_in_20_perc <- 0
  super_temp$LR_in_30_perc <- 0
  super_temp$LR_in_40_perc <- 0
  super_temp$LR_in_50_perc <- 0
  super_temp$LR_in_60_perc <- 0
  super_temp$LR_in_70_perc <- 0
  super_temp$LR_in_80_perc <- 0
  super_temp$LR_in_90_perc <- 0
  super_temp$LR_in_100_perc <- 0

  for(e in 1:nrow(super_temp)){
     if (super_temp$insertion_position[e] < (MR_length/10)){
      super_temp$LR_in_10_perc[e] <- 1
     } else {super_temp$LR_in_10_perc[e] <- 0}
    if (super_temp$insertion_position[e] < 2*(MR_length/10) & super_temp$insertion_position[e] > (MR_length/10)){
      super_temp$LR_in_20_perc[e] <- 1
    } else {super_temp$LR_in_20_perc[e] <- 0}
    if (super_temp$insertion_position[e] < 3*(MR_length/10) & super_temp$insertion_position[e] > 2*(MR_length/10)){
      super_temp$LR_in_30_perc[e] <- 1
    } else {super_temp$LR_in_30_perc[e] <- 0}
    if (super_temp$insertion_position[e] < 4*(MR_length/10) & super_temp$insertion_position[e] > 3*(MR_length/10)){
      super_temp$LR_in_40_perc[e] <- 1
    } else {super_temp$LR_in_40_perc[e] <- 0}
    if (super_temp$insertion_position[e] < 5*(MR_length/10) & super_temp$insertion_position[e] > 4*(MR_length/10)){
      super_temp$LR_in_50_perc[e] <- 1
    } else {super_temp$LR_in_50_perc[e] <- 0}
    if (super_temp$insertion_position[e] < 6*(MR_length/10) & super_temp$insertion_position[e] > 5*(MR_length/10)){
      super_temp$LR_in_60_perc[e] <- 1
    } else {super_temp$LR_in_60_perc[e] <- 0}
    if (super_temp$insertion_position[e] < 7*(MR_length/10) & super_temp$insertion_position[e] > 6*(MR_length/10)){
      super_temp$LR_in_70_perc[e] <- 1
    } else {super_temp$LR_in_70_perc[e] <- 0}
    if (super_temp$insertion_position[e] < 8*(MR_length/10) & super_temp$insertion_position[e] > 7*(MR_length/10)){
      super_temp$LR_in_80_perc[e] <- 1
    } else {super_temp$LR_in_80_perc[e] <- 0}
    if (super_temp$insertion_position[e] < 9*(MR_length/10) & super_temp$insertion_position[e] > 8*(MR_length/10)){
      super_temp$LR_in_90_perc[e] <- 1
    } else {super_temp$LR_in_90_perc[e] <- 0}
    if (super_temp$insertion_position[e] < 10*(MR_length/10) & super_temp$insertion_position[e] > 9*(MR_length/10)){
      super_temp$LR_in_100_perc[e] <- 1
    } else {super_temp$LR_in_100_perc[e] <- 0}
  } 

  super_temp$LRL_in_10_perc <- super_temp$length * super_temp$LR_in_10_perc
  super_temp$LRL_in_20_perc <- super_temp$length * super_temp$LR_in_20_perc
  super_temp$LRL_in_30_perc <- super_temp$length * super_temp$LR_in_30_perc
  super_temp$LRL_in_40_perc <- super_temp$length * super_temp$LR_in_40_perc
  super_temp$LRL_in_50_perc <- super_temp$length * super_temp$LR_in_50_perc
  super_temp$LRL_in_60_perc <- super_temp$length * super_temp$LR_in_60_perc
  super_temp$LRL_in_70_perc <- super_temp$length * super_temp$LR_in_70_perc
  super_temp$LRL_in_80_perc <- super_temp$length * super_temp$LR_in_80_perc
  super_temp$LRL_in_90_perc <- super_temp$length * super_temp$LR_in_90_perc
  super_temp$LRL_in_100_perc <- super_temp$length * super_temp$LR_in_100_perc
  
  LR_no_10_100 <- sum(super_temp$LR_in_10_perc)
  LR_no_20_100 <- sum(super_temp$LR_in_20_perc)
  LR_no_30_100 <- sum(super_temp$LR_in_30_perc)
  LR_no_40_100 <- sum(super_temp$LR_in_40_perc)
  LR_no_50_100 <- sum(super_temp$LR_in_50_perc)
  LR_no_60_100 <- sum(super_temp$LR_in_60_perc)
  LR_no_70_100 <- sum(super_temp$LR_in_70_perc)
  LR_no_80_100 <- sum(super_temp$LR_in_80_perc)
  LR_no_90_100 <- sum(super_temp$LR_in_90_perc)
  LR_no_100_100 <- sum(super_temp$LR_in_100_perc)
  
  LRL_10_100 <- sum(super_temp$LRL_in_10_perc)
  LRL_20_100 <- sum(super_temp$LRL_in_20_perc)
  LRL_30_100 <- sum(super_temp$LRL_in_30_perc)
  LRL_40_100 <- sum(super_temp$LRL_in_40_perc)
  LRL_50_100 <- sum(super_temp$LRL_in_50_perc)
  LRL_60_100 <- sum(super_temp$LRL_in_60_perc)
  LRL_70_100 <- sum(super_temp$LRL_in_70_perc)
  LRL_80_100 <- sum(super_temp$LRL_in_80_perc)
  LRL_90_100 <- sum(super_temp$LRL_in_90_perc)
  LRL_100_100 <- sum(super_temp$LRL_in_100_perc)
  
  super_temp$momentum <- (super_temp$insertion_position * super_temp$length)
  all_momentum <- sum(super_temp$momentum)
  all_length <- sum(super_temp$length)
  CoG <- (all_momentum / all_length)  
  
  if(dim(super_temp)[1] > 2){
  model <- lm(super_temp$length ~ super_temp$insertion_position)
  LRL.dec <- model$coefficients[[2]]
  LRL.dec.R2 <- summary(model)$r.squared
  } else {
    LRL.dec <- "n.a."
    LRL.dec.R2 <- "n.a."
  }
  
  
  only_MR$Apical[i] <- Apical
  only_MR$Branched[i] <- Branched
  only_MR$Basal[i] <- Basal
  
  only_MR$Apical_perc[i] <- (Apical / MR_length)
  only_MR$Branched_perc[i] <- (Branched / MR_length)
  only_MR$Basal_perc[i] <- (Basal / MR_length)
  only_MR$LR_no_10_100[i] <- LR_no_10_100
  only_MR$LR_no_20_100[i] <- LR_no_20_100
  only_MR$LR_no_30_100[i] <- LR_no_30_100
  only_MR$LR_no_40_100[i] <- LR_no_40_100
  only_MR$LR_no_50_100[i] <- LR_no_50_100
  only_MR$LR_no_60_100[i] <- LR_no_60_100
  only_MR$LR_no_70_100[i] <- LR_no_70_100
  only_MR$LR_no_80_100[i] <- LR_no_80_100
  only_MR$LR_no_90_100[i] <- LR_no_90_100
  only_MR$LR_no_100_100[i] <- LR_no_100_100
  
  only_MR$LRL_10_100[i] <- LRL_10_100
  only_MR$LRL_20_100[i] <- LRL_20_100
  only_MR$LRL_30_100[i] <- LRL_30_100
  only_MR$LRL_40_100[i] <- LRL_40_100
  only_MR$LRL_50_100[i] <- LRL_50_100
  only_MR$LRL_60_100[i] <- LRL_60_100
  only_MR$LRL_70_100[i] <- LRL_70_100
  only_MR$LRL_80_100[i] <- LRL_80_100
  only_MR$LRL_90_100[i] <- LRL_90_100
  only_MR$LRL_100_100[i] <- LRL_100_100
  
  only_MR$CoG[i] <- CoG
  only_MR$LRL.dec[i] <- LRL.dec
  only_MR$LRL.dec.R2[i] <- LRL.dec.R2  
}

```

let's have a look at the entire table:

```{r}
head(only_MR)
tail(only_MR)
```

then- we need to fuse all the main roots that dont own LR

Make sure that the data structure is the same as for the only_MR file

```{r}
head(no_LR_MR)
```
let's add missing collumns:

```{r}
no_LR_MR$Apical <- "n.a."
no_LR_MR$Branched <- "n.a."
no_LR_MR$Basal <- "n.a."

no_LR_MR$Apical_perc <- "n.a."
no_LR_MR$Branched_perc <- "n.a."
no_LR_MR$Basal_perc <- "n.a."

no_LR_MR$LR_no_10_100 <- 0
no_LR_MR$LR_no_20_100 <- 0
no_LR_MR$LR_no_30_100 <- 0
no_LR_MR$LR_no_40_100 <- 0
no_LR_MR$LR_no_50_100 <- 0
no_LR_MR$LR_no_60_100 <- 0
no_LR_MR$LR_no_70_100 <- 0
no_LR_MR$LR_no_80_100 <- 0
no_LR_MR$LR_no_90_100 <- 0
no_LR_MR$LR_no_100_100 <- 0

no_LR_MR$LRL_10_100 <- 0
no_LR_MR$LRL_20_100 <- 0
no_LR_MR$LRL_30_100 <- 0
no_LR_MR$LRL_40_100 <- 0
no_LR_MR$LRL_50_100 <- 0
no_LR_MR$LRL_60_100 <- 0
no_LR_MR$LRL_70_100 <- 0
no_LR_MR$LRL_80_100 <- 0
no_LR_MR$LRL_90_100 <- 0
no_LR_MR$LRL_100_100 <- 0

no_LR_MR$CoG <- 0
no_LR_MR$LRL.dec <- "n.a."
no_LR_MR$LRL.dec.R2 <- "n.a."
```

finally - let’s fuse the two files together:


```{r}
colnames(only_MR)
colnames(no_LR_MR)

only_MR <- only_MR[,c(1:12,14:42)]
```


```{r}
all_MR <- rbind(only_MR, no_LR_MR)
head(all_MR)
dim(all_MR)
length(unique(all_MR$root_name))
length(unique(all_MR$image))

length(unique(all_MR$image))/length(unique(all_MR$root_name))
```
Let's add some more interesting columns:

```{r}
all_MR$LRL <- (all_MR$LRL_10_100 + all_MR$LRL_20_100 + all_MR$LRL_30_100 + all_MR$LRL_40_100 + all_MR$LRL_50_100 + all_MR$LRL_60_100 + all_MR$LRL_70_100 + all_MR$LRL_80_100 + all_MR$LRL_90_100 + all_MR$LRL_100_100)
all_MR$LRno <- (all_MR$LR_no_10_100 + all_MR$LR_no_20_100 + all_MR$LR_no_30_100 + all_MR$LR_no_40_100 + all_MR$LR_no_50_100 + all_MR$LR_no_60_100 + all_MR$LR_no_70_100 + all_MR$LR_no_80_100 + all_MR$LR_no_90_100 + all_MR$LR_no_100_100)
all_MR$aLRL <- all_MR$LRL / all_MR$LRno
all_MR$TRS <- all_MR$MRL + all_MR$LRL
head(all_MR)
```

# data visualization

OK - now we have all the traits calculated - but we are still missing the DAY information - as in DAY after stress. So let's calculate it per experiment:

```{r}
head(all_MR)
unique(all_MR[,c("experiment", "date")])
```

based on the above - we have the following

day      Big01      Big02     Big03      Big04
day 0 - 20190909   20190923  20190930  20191008
day 1 - 20190910   20190924  20191001  20191009
day 2 - 20190911   20190925  20191002  20191010
day 3 - 20190912   20190926  20191003  20191011
day 4 - 20190913   20190927  20191004  20191012

So now we have to do conditional formating for DAS (Days After Stress):

```{r}
all_MR$DAS <- all_MR$date
all_MR$DAS <- gsub("20190909", "0", all_MR$DAS)
all_MR$DAS <- gsub("20190923", "0", all_MR$DAS)
all_MR$DAS <- gsub("20190930", "0", all_MR$DAS)
all_MR$DAS <- gsub("20191008", "0", all_MR$DAS)

all_MR$DAS <- gsub("20190910", "1", all_MR$DAS)
all_MR$DAS <- gsub("20190924", "1", all_MR$DAS)
all_MR$DAS <- gsub("20191001", "1", all_MR$DAS)
all_MR$DAS <- gsub("20191009", "1", all_MR$DAS)

all_MR$DAS <- gsub("20190911", "2", all_MR$DAS)
all_MR$DAS <- gsub("20190925", "2", all_MR$DAS)
all_MR$DAS <- gsub("20191002", "2", all_MR$DAS)
all_MR$DAS <- gsub("20191010", "2", all_MR$DAS)

all_MR$DAS <- gsub("20190912", "3", all_MR$DAS)
all_MR$DAS <- gsub("20190926", "3", all_MR$DAS)
all_MR$DAS <- gsub("20191003", "3", all_MR$DAS)
all_MR$DAS <- gsub("20191011", "3", all_MR$DAS)

all_MR$DAS <- gsub("20190913", "4", all_MR$DAS)
all_MR$DAS <- gsub("20190927", "4", all_MR$DAS)
all_MR$DAS <- gsub("20191004", "4", all_MR$DAS)
all_MR$DAS <- gsub("20191012", "4", all_MR$DAS)

unique(all_MR[,c("experiment", "DAS")])
```


before we go any further - let's clean up this data and add whatever else missing traits:

```{r}
colnames(all_MR)
dim(all_MR)
all_MR2 <- all_MR[,c(1:4, 46, 6:9, 13:45)]
head(all_MR2)
all_MR2$MRL.p.TRS <- all_MR2$MRL / all_MR2$TRS
all_MR2$aLRL.p.TRS <- all_MR2$aLRL / all_MR2$TRS
all_MR2$LRL_10_perc <- all_MR2$LRL_10_100 / all_MR2$LRL
all_MR2$LRL_20_perc <- all_MR2$LRL_20_100 / all_MR2$LRL
all_MR2$LRL_30_perc <- all_MR2$LRL_30_100 / all_MR2$LRL
all_MR2$LRL_40_perc <- all_MR2$LRL_40_100 / all_MR2$LRL
all_MR2$LRL_50_perc <- all_MR2$LRL_50_100 / all_MR2$LRL
all_MR2$LRL_60_perc <- all_MR2$LRL_60_100 / all_MR2$LRL
all_MR2$LRL_70_perc <- all_MR2$LRL_70_100 / all_MR2$LRL
all_MR2$LRL_80_perc <- all_MR2$LRL_80_100 / all_MR2$LRL
all_MR2$LRL_90_perc <- all_MR2$LRL_90_100 / all_MR2$LRL
all_MR2$LRL_100_perc <- all_MR2$LRL_100_100 / all_MR2$LRL

all_MR2$LRno_10_perc <- all_MR2$LR_no_10_100 / all_MR2$LRno
all_MR2$LRno_20_perc <- all_MR2$LR_no_20_100 / all_MR2$LRno
all_MR2$LRno_30_perc <- all_MR2$LR_no_30_100 / all_MR2$LRno
all_MR2$LRno_40_perc <- all_MR2$LR_no_40_100 / all_MR2$LRno
all_MR2$LRno_50_perc <- all_MR2$LR_no_50_100 / all_MR2$LRno
all_MR2$LRno_60_perc <- all_MR2$LR_no_60_100 / all_MR2$LRno
all_MR2$LRno_70_perc <- all_MR2$LR_no_70_100 / all_MR2$LRno
all_MR2$LRno_80_perc <- all_MR2$LR_no_80_100 / all_MR2$LRno
all_MR2$LRno_90_perc <- all_MR2$LR_no_90_100 / all_MR2$LRno
all_MR2$LRno_100_perc <- all_MR2$LR_no_100_100 / all_MR2$LRno

head(all_MR2)
```

Just before we start plotting the data - let's also check the genotype information whether this all makes sense and if we are not missing any info:

```{r}
unique(all_MR2$genotype)
unique(all_MR$root_name)

all_MR2$genotype <- gsub("2800", "280", all_MR2$genotype)
all_MR2$root_name <- gsub(" 089_1_", " 089_1_C", all_MR2$root_name)
all_MR2$root_name <- gsub(" 089_1_C_C", " 089_1_C", all_MR2$root_name)
all_MR2$root_name <- gsub(" 089_1_C_S", " 089_1_S", all_MR2$root_name)

all_MR2$root_name <- gsub("301_3", "301_3_C", all_MR2$root_name)
all_MR2$root_name <- gsub("301_3_CC", "301_3_C", all_MR2$root_name)
all_MR2$root_name <- gsub("301_3_CS", "301_3_S", all_MR2$root_name)

unique(all_MR2$cond)
all_MR2$cond <- gsub("s", "S", all_MR2$cond)
unique(all_MR2$cond)

normal_cond <- c("C", "S")
thats_it <- subset(all_MR2, (all_MR2$cond %in% normal_cond))
whats_that <- subset(all_MR2, !(all_MR2$cond %in% normal_cond))
whats_that
whats_that$cond <- "C"

all_MR3 <- rbind(thats_it, whats_that)
unique(all_MR3$cond)
```


## Histograms

### Library loading
OK - now we can start plotting - finally! loading the libraries

```{r}
library(ggplot2)
library(ggpubr)
library(doBy)
library(cowplot)
library(RColorBrewer)
library(corrplot)
library(ggbeeswarm)
library(RColorBrewer)
library(ggridges)
library(gapminder)
library("gplots")
library("colorRamps")
```

Let's start from a simple histogram - so we can see the treatment dependent change in TRS

```{r}
TRS_histo <- gghistogram(all_MR3, x = "TRS",
            add = "mean", rug = TRUE,
            color = "cond", fill = "cond", facet.by = "DAS", 
            palette = c("#00AFBB", "#E7B800")) + xlab("Total root length (cm)")
TRS_histo
```

OK - looks as we expect it to. Let's have a look at other things - like LR number:

```{r}
LRno_histo <- gghistogram(all_MR3, x = "LRno",
            add = "mean", rug = TRUE,
            color = "cond", fill = "cond", facet.by = "DAS",
            palette = c("#00AFBB", "#E7B800")) + xlab("number of LR / Main Root")
LRno_histo
```

```{r}
LRL_histo <- gghistogram(all_MR3, x = "LRL",
            add = "mean", rug = TRUE,
            color = "cond", fill = "cond", facet.by = "DAS",
            palette = c("#00AFBB", "#E7B800")) + xlab("Lateral Root Length (cm)")
LRL_histo
```

```{r}
MRL_histo <- gghistogram(all_MR3, x = "MRL",
            add = "mean", rug = TRUE,
            color = "cond", fill = "cond", facet.by = "DAS",
            palette = c("#00AFBB", "#E7B800")) + xlab("Main root length (cm)")
MRL_histo
```

```{r}
CoG_histo <- gghistogram(all_MR3, x = "CoG",
            add = "mean", rug = TRUE,
            color = "cond", fill = "cond", facet.by = "DAS",
            palette = c("#00AFBB", "#E7B800")) + xlab("Center of Gravity (cm of MR)")
CoG_histo
```

```{r}
all_MR3$LRL.dec <- as.numeric(all_MR3$LRL.dec)
LRL.dec_histo <- gghistogram(all_MR3, x = "LRL.dec",
            add = "mean", rug = TRUE,
            color = "cond", fill = "cond", facet.by = "DAS",
            palette = c("#00AFBB", "#E7B800")) + xlab("Lateral Root Length decrease over MR (cm / cm of MR)")
LRL.dec_histo
```

the above doesnt really make sense - let's replace all these extreme values (< -2, > 2) with "n.a.")

```{r}
temp <- subset(all_MR3, all_MR3$LRL.dec < 2)
temp <- subset(temp, temp$LRL.dec > -2)

LRL.dec_histo <- gghistogram(temp, x = "LRL.dec",
            add = "mean", rug = TRUE,
            color = "cond", fill = "cond", facet.by = "DAS",
            palette = c("#00AFBB", "#E7B800")) + xlab("Lateral Root Length decrease over MR (cm / cm of MR)")
LRL.dec_histo

```



```{r}
all_MR3$LRL.dec.R2 <- as.numeric(all_MR3$LRL.dec.R2)
LRL.dec.R2_histo <- gghistogram(all_MR3, x = "LRL.dec.R2",
                                add = "mean", rug = TRUE,
                                color = "cond", fill = "cond", facet.by = "DAS",
                                palette = c("#00AFBB", "#E7B800")) + xlab("R2 of Lateral Root Length decrease over MR")
LRL.dec.R2_histo
```

## visualization of changes in RSA across time:

Let's try to visualize changes across the accessions throughout the time using the line graph - similar to ones in Awlia et al., 2020

First - what we need to do is to calculate the average per line and per condition:

```{r}
colnames(all_MR3)
library(doBy)

MR_sum <- summaryBy(data = all_MR3, MRL + LRL + LRno + aLRL + TRS + MRL.p.TRS + aLRL.p.TRS + CoG + LRL.dec ~ genotype + cond + DAS)
head(MR_sum)
MR_sum$geno_cond <- paste(MR_sum$genotype, "_", MR_sum$cond, sep="")
```

```{r}
TRS_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=TRS.mean, group = geno_cond, color = cond)) 
TRS_lgraph <- TRS_lgraph +geom_line(alpha = 0.1) 
TRS_lgraph <- TRS_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)+ stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
TRS_lgraph <- TRS_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
TRS_lgraph <- TRS_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
TRS_lgraph <- TRS_lgraph + ylab("Total Root Size (cm)") + xlab("Days After Stress") + theme(legend.position='none')
TRS_lgraph
```

```{r}
MRL_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=MRL.mean, group = geno_cond, color = cond)) 
MRL_lgraph <- MRL_lgraph +geom_line(alpha = 0.1) 
MRL_lgraph <- MRL_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
MRL_lgraph <- MRL_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
MRL_lgraph <- MRL_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
MRL_lgraph <- MRL_lgraph + ylab("Main Root Length (cm)") + xlab("Days After Stress") + theme(legend.position='none')
MRL_lgraph
```


```{r}
LRL_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=LRL.mean, group = geno_cond, color = cond)) 
LRL_lgraph <- LRL_lgraph +geom_line(alpha = 0.1) 
LRL_lgraph <- LRL_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)+ stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRL_lgraph <- LRL_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_lgraph <- LRL_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
LRL_lgraph <- LRL_lgraph + ylab("Lateral Root Length (cm)") + xlab("Days After Stress") + theme(legend.position='none')
LRL_lgraph
```


```{r}
LRno_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=LRno.mean, group = geno_cond, color = cond)) 
LRno_lgraph <- LRno_lgraph +geom_line(alpha = 0.1) 
LRno_lgraph <- LRno_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRno_lgraph <- LRno_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_lgraph <- LRno_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
LRno_lgraph <- LRno_lgraph + ylab("Lateral Root Number (per MR)") + xlab("Days After Stress") + theme(legend.position='none')
LRno_lgraph
```

```{r}
CoG_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=CoG.mean, group = geno_cond, color = cond)) 
CoG_lgraph <- CoG_lgraph +geom_line(alpha = 0.1) 
CoG_lgraph <- CoG_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
CoG_lgraph <- CoG_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
CoG_lgraph <- CoG_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
CoG_lgraph <- CoG_lgraph + ylab("Center of Gravity (cm of MR)") + xlab("Days After Stress") + theme(legend.position='none')
CoG_lgraph
```

```{r}
LRL.dec_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=LRL.dec.mean, group = geno_cond, color = cond)) 
LRL.dec_lgraph <- LRL.dec_lgraph +geom_line(alpha = 0.1) 
LRL.dec_lgraph <- LRL.dec_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRL.dec_lgraph <- LRL.dec_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL.dec_lgraph <- LRL.dec_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
LRL.dec_lgraph <- LRL.dec_lgraph + ylab("decrease of LRL over MRL (cm / cm of MR)") + xlab("Days After Stress") + theme(legend.position='none')
LRL.dec_lgraph
```

```{r}
MRL.p.TRS_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=MRL.p.TRS.mean, group = geno_cond, color = cond)) 
MRL.p.TRS_lgraph <- MRL.p.TRS_lgraph +geom_line(alpha = 0.1) 
MRL.p.TRS_lgraph <- MRL.p.TRS_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
MRL.p.TRS_lgraph <- MRL.p.TRS_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
MRL.p.TRS_lgraph <- MRL.p.TRS_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
MRL.p.TRS_lgraph <- MRL.p.TRS_lgraph + ylab("ratio (MRL / TRS)") + xlab("Days After Stress") + theme(legend.position='none')
MRL.p.TRS_lgraph
```

```{r}
aLRL.p.TRS_lgraph <- ggplot(data=MR_sum, aes(x= DAS, y=aLRL.p.TRS.mean, group = geno_cond, color = cond)) 
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph +geom_line(alpha = 0.1) 
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Average per accession")
aLRL.p.TRS_lgraph <- aLRL.p.TRS_lgraph + ylab("ratio (aLRL / TRS)") + xlab("Days After Stress") + theme(legend.position='none')
aLRL.p.TRS_lgraph
```

Lets combine these into one figure:

```{r}
pdf("Fig.TRS_MRL_LRL_LRno_CoG_MRLpTRS_Big01_to_Big04.pdf", height = 20, width = 15)
plot_grid(TRS_lgraph, MRL_lgraph,
          LRL_lgraph, LRno_lgraph,
          CoG_lgraph, MRL.p.TRS_lgraph, ncol=2, labels = "AUTO")
dev.off()
```


OK - let's keep going with exploring other cool traits...

## Vizualization of LRL/LRno distribution across the root:

In order to examine how the LR is distributed across MR - lets extract all the LR distribution traits

```{r}
colnames(all_MR3)
dim(all_MR3)
LR_distribution <- all_MR3[,c(1:8, 16:64)]
colnames(LR_distribution)
```

Cool - then let's extract only the last day, reshape the data and plot the absolute LRno and LRL across the MR portions:

### 4 days after stress

```{r}
day4 <- subset(LR_distribution, LR_distribution$DAS == 4)
head(day4)
colnames(day4)
LRno <- day4[,c(1:18)]
head(LRno)
LRL <- day4[,c(1:8,19:28)]
library(reshape2)
mLRno <- melt(LRno, value.name = "LRno")
mLRL <- melt(LRL, value.name = "LRL")
head(mLRno)
head(mLRL)
```

cool - now let's adjust the portion of the MR that is being used to calculate the LR distribution in "variable" columns:

```{r}
mLRno$variable <- gsub("LR_no_", "", mLRno$variable)
mLRno$variable <- gsub("_100", "", mLRno$variable)

mLRL$variable <- gsub("LRL_", "", mLRL$variable)
mLRL$variable <- gsub("_100", "", mLRL$variable)

mLRL$variable <- as.numeric(mLRL$variable)
mLRno$variable <- as.numeric(mLRno$variable)
colnames(mLRL)[9] <- "perc.of.MRL"
colnames(mLRno)[9] <- "perc.of.MRL"
head(mLRL)
head(mLRno)

```

```{r}
LRL_distribution_graph_d4 <- ggplot(data=mLRL, aes(x= perc.of.MRL, y=LRL, group = root_name, color = cond)) 
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) 
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2)  + ggtitle("4 Days After Stress")
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + theme(legend.position="none")+ labs(color = "Treatment")
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + ylab("Lateral Root Length (cm)") + xlab("Portion of Main Root Length (%)") 
LRL_distribution_graph_d4 <- LRL_distribution_graph_d4 + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_distribution_graph_d4
```

```{r}
LRno_distribution_graph_d4 <- ggplot(data=mLRno, aes(x= perc.of.MRL, y=LRno, group = root_name, color = cond)) 
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) 
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("4 Days After Stress")
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + theme(legend.position="none")+ labs(color = "Treatment")
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + ylab("Lateral Root number") + xlab("Portion of Main Root Length (%)") 
LRno_distribution_graph_d4 <- LRno_distribution_graph_d4 + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_distribution_graph_d4
```

all good - but I think it would be better to calculate the above as %of LRL or %LRno per plant - then we can get better idea for clustering:

```{r}
colnames(day4)
LRno_perc <- day4[,c(1:8,48:57)]
LRL_perc <- day4[,c(1:8,38:47)]
head(LRno_perc)
head(LRL_perc)
```


```{r}
mLRno_perc <- melt(LRno_perc, value.name = "LRno.perc")
mLRL_perc <- melt(LRL_perc, value.name = "LRL.perc")

head(mLRno_perc)
head(mLRL_perc)
unique(mLRno_perc$variable)

mLRno_perc$variable <- gsub("LRno_", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("LRL_", "", mLRL_perc$variable)

mLRno_perc$variable <- gsub("_perc", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("_perc", "", mLRL_perc$variable)

mLRno_perc$variable <- as.numeric(mLRno_perc$variable)
mLRL_perc$variable <- as.numeric(mLRL_perc$variable)

colnames(mLRno_perc)[9] <- "perc.of.MRL"
colnames(mLRL_perc)[9] <- "perc.of.MRL"
head(mLRno_perc)
head(mLRL_perc)
```

OK - now let's recalculate these graphs:

```{r}
LRL_distribution_graph_d4_perc <- ggplot(data=mLRL_perc, aes(x= perc.of.MRL, y=LRL.perc, group = root_name, color = cond)) 
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) 
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("4 Days After Stress")
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + ylab("Lateral Root Length (fraction of total LRL)") + xlab("Portion of Main Root Length (%)") 
LRL_distribution_graph_d4_perc <- LRL_distribution_graph_d4_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_distribution_graph_d4_perc


LRno_distribution_graph_d4_perc <- ggplot(data=mLRno_perc, aes(x= perc.of.MRL, y=LRno.perc, group = root_name, color = cond)) 
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) 
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("4 Days After Stress")
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + ylab("Lateral Root number (fraction of total LR #)") + xlab("Portion of Main Root Length (%)") 
LRno_distribution_graph_d4_perc <- LRno_distribution_graph_d4_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_distribution_graph_d4_perc
```
OK - so the percentage of LRL is definitely more informative. Let's have a look how it looks in earlier days:

### 3 days after stress

```{r}
day3 <- subset(all_MR3, all_MR3$DAS == 3)
LRno_perc <- day3[,c(1:8,48:57)]
LRL_perc <- day3[,c(1:8,38:47)]
mLRno_perc <- melt(LRno_perc, value.name = "LRno.perc")
mLRL_perc <- melt(LRL_perc, value.name = "LRL.perc")

mLRno_perc$variable <- gsub("LRno_", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("LRL_", "", mLRL_perc$variable)

mLRno_perc$variable <- gsub("_perc", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("_perc", "", mLRL_perc$variable)

mLRno_perc$variable <- as.numeric(mLRno_perc$variable)
mLRL_perc$variable <- as.numeric(mLRL_perc$variable)

colnames(mLRno_perc)[9] <- "perc.of.MRL"
colnames(mLRL_perc)[9] <- "perc.of.MRL"

LRL_distribution_graph_d3_perc <- ggplot(data=mLRL_perc, aes(x= perc.of.MRL, y=LRL.perc, group = root_name, color = cond)) 
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("3 Days After Stress")
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + ylab("Lateral Root Length (fraction of total LRL)") + xlab("Portion of Main Root Length (%)") 
LRL_distribution_graph_d3_perc <- LRL_distribution_graph_d3_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_distribution_graph_d3_perc


LRno_distribution_graph_d3_perc <- ggplot(data=mLRno_perc, aes(x= perc.of.MRL, y=LRno.perc, group = root_name, color = cond)) 
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("3 Days After Stress")
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + ylab("Lateral Root number (fraction of total LR #)") + xlab("Portion of Main Root Length (%)") 
LRno_distribution_graph_d3_perc <- LRno_distribution_graph_d3_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_distribution_graph_d3_perc
```


```{r}
day2 <- subset(all_MR3, all_MR3$DAS == 2)
LRno_perc <- day2[,c(1:8,48:57)]
LRL_perc <- day2[,c(1:8,38:47)]
mLRno_perc <- melt(LRno_perc, value.name = "LRno.perc")
mLRL_perc <- melt(LRL_perc, value.name = "LRL.perc")

mLRno_perc$variable <- gsub("LRno_", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("LRL_", "", mLRL_perc$variable)

mLRno_perc$variable <- gsub("_perc", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("_perc", "", mLRL_perc$variable)

mLRno_perc$variable <- as.numeric(mLRno_perc$variable)
mLRL_perc$variable <- as.numeric(mLRL_perc$variable)

colnames(mLRno_perc)[9] <- "perc.of.MRL"
colnames(mLRL_perc)[9] <- "perc.of.MRL"

LRL_distribution_graph_d2_perc <- ggplot(data=mLRL_perc, aes(x= perc.of.MRL, y=LRL.perc, group = root_name, color = cond)) 
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("2 Days After Stress")
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + ylab("Lateral Root Length (fraction of total LRL)") + xlab("Portion of Main Root Length (%)") 
LRL_distribution_graph_d2_perc <- LRL_distribution_graph_d2_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_distribution_graph_d2_perc


LRno_distribution_graph_d2_perc <- ggplot(data=mLRno_perc, aes(x= perc.of.MRL, y=LRno.perc, group = root_name, color = cond)) 
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("2 Days After Stress")
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + ylab("Lateral Root number (fraction of total LR #)") + xlab("Portion of Main Root Length (%)") 
LRno_distribution_graph_d2_perc <- LRno_distribution_graph_d2_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_distribution_graph_d2_perc
```

```{r}
day1 <- subset(all_MR3, all_MR3$DAS == 1)
LRno_perc <- day1[,c(1:8,48:57)]
LRL_perc <- day1[,c(1:8,38:47)]
mLRno_perc <- melt(LRno_perc, value.name = "LRno.perc")
mLRL_perc <- melt(LRL_perc, value.name = "LRL.perc")

mLRno_perc$variable <- gsub("LRno_", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("LRL_", "", mLRL_perc$variable)

mLRno_perc$variable <- gsub("_perc", "", mLRno_perc$variable)
mLRL_perc$variable <- gsub("_perc", "", mLRL_perc$variable)

mLRno_perc$variable <- as.numeric(mLRno_perc$variable)
mLRL_perc$variable <- as.numeric(mLRL_perc$variable)

colnames(mLRno_perc)[9] <- "perc.of.MRL"
colnames(mLRL_perc)[9] <- "perc.of.MRL"

LRL_distribution_graph_d1_perc <- ggplot(data=mLRL_perc, aes(x= perc.of.MRL, y=LRL.perc, group = root_name, color = cond)) 
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("1 Day After Stress")
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + ylab("Lateral Root Length (fraction of total LRL)") + xlab("Portion of Main Root Length (%)") 
LRL_distribution_graph_d1_perc <- LRL_distribution_graph_d1_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRL_distribution_graph_d1_perc


LRno_distribution_graph_d1_perc <- ggplot(data=mLRno_perc, aes(x= perc.of.MRL, y=LRno.perc, group = root_name, color = cond)) 
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + geom_line(alpha = 0.1) + scale_colour_manual(values = c("dodgerblue3", "tomato1")) + ylim(0,1)
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + stat_summary(geom = "ribbon", linetype=0, fun.data = mean_cl_normal, aes(group= cond), alpha = 0.2) + ggtitle("1 Day After Stress")
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + theme(legend.position="none")+ labs(color = "Treatment")
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + ylab("Lateral Root number (fraction of total LR #)") + xlab("Portion of Main Root Length (%)") 
LRno_distribution_graph_d1_perc <- LRno_distribution_graph_d1_perc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
LRno_distribution_graph_d1_perc
```

ok - maybe let's combine these graphs into a figure thus far:

```{r}
library(cowplot)
pdf("Fig.Lateral_root_distribution_across_MRL_DAS1_DAS4_Big01_to_Big04.pdf", height = 20, width = 15)
plot_grid(LRL_distribution_graph_d1_perc, LRno_distribution_graph_d1_perc,
          LRL_distribution_graph_d2_perc, LRno_distribution_graph_d2_perc,
          LRL_distribution_graph_d3_perc, LRno_distribution_graph_d3_perc,
          LRL_distribution_graph_d4_perc, LRno_distribution_graph_d4_perc, ncol=2, labels = "AUTO")
dev.off()
```



# Calculating growth rates for each plant:

OK - so now we have all the data organized and everything seems to look pretty sane - let's start calculating the growth rate of aLRL, LR# and MR. The problem is that in some cases - the MR grew into the plate - and we didnt continue tracing it - so the length is getting staled. So - in these cases - I would like to calculate the growth rate only over the period where the size of the root is actually increasing. So:

First - set the "day" ad numeric variable - because this will later come and bite us in our asses:

```{r}
all_MR3$DAS <- as.numeric(as.character(all_MR3$DAS))
head(all_MR3)
```
## Establishing calculations

Second - isolate one plant from the dataset, sort per day, and mark which of the values for MR are duplicates:
```{r}
temp1 <- subset(all_MR3, all_MR3$root == unique(all_MR3$root)[100])
temp2 <- temp1[order(temp1$DAS),]
head(temp2)

temp2$MRdouble <- "no"
for(i in 2:5){
 if(temp2$MRL[i] == temp2$MRL[i-1]){
  temp2$MRdouble[i] <- "yes"   
 } 
  else{
  temp2$MRdouble[i] <- "no"   
 } 
}

temp3 <- subset(temp2, temp2$MRdouble == "no")
temp3
```

Now that we have the dataset without duplicated values - let's calculate MR growth rate using linear function:

```{r}
plot(temp3$MRL ~ temp3$DAS)
abline(lm(temp3$MRL ~ temp3$DAS))
```

Let's check if this is also true for #LR:

```{r}
temp2$LRnodouble <- "no"
for(i in 2:5){
 if(temp2$LRno[i] == temp2$LRno[i-1]){
  temp2$LRnodouble[i] <- "yes"   
 } 
  else{
  temp2$LRnodouble[i] <- "no"   
 } 
}

temp3 <- subset(temp2, temp2$LRnodouble == "no")

plot(temp3$LRno ~ temp3$DAS)
abline(lm(temp3$LRno ~ temp3$DAS))
```
and for aLRL:

```{r}
temp2$aLRLdouble <- "no"
for(i in 2:5){
 if(temp2$aLRL[i] == temp2$aLRL[i-1]){
  temp2$aLRLdouble[i] <- "yes"   
 } 
  else{
  temp2$aLRLdouble[i] <- "no"   
 } 
}
temp2

temp3 <- subset(temp2, temp2$aLRLdouble == "no")

plot(temp3$aLRL ~ temp3$DAS)
abline(lm(temp3$aLRL ~ temp3$DAS))
```

## Looped linear modeling for all RSA components

First - let's construct the dataframe which will hold all the data points:

```{r}
names <- c(text="root_name", "genotype", "cond", "MR.intercept", "MR.delta", "LRno.intercept", "LRno.delta", "aLRL.intercept", "aLRL.delta")
growth_factors <- data.frame()

for (k in names) growth_factors[[k]] <- as.character()

```

just few fixes:

```{r}
all_MR3$root_name<- gsub("2800_3_S", "280_3_S", all_MR3$root_name)
all_MR3 <- subset(all_MR3, all_MR3$image != "_set1_day1_20190930_093.rsml")
all_MR3 <- subset(all_MR3, all_MR3$image != "_set1_day1_20190930_090.rsml")	
all_MR3 <- subset(all_MR3, all_MR3$image != "_set1_day1_20190930_067.rsml")
all_MR3 <- subset(all_MR3, all_MR3$image != "_set1_day1_20191008_047_FU.rsml")

```


Then - let's loop it in for calculating the Main Root growth factors:

```{r}
for(e in c(1:length(unique(all_MR3$root_name)))){
  temp1 <- subset(all_MR3, all_MR3$root_name == unique(all_MR3$root_name)[e])
  temp2 <- temp1[order(temp1$DAS),]
  dim(temp2)
  if(dim(temp2)[1] < 2){
    
  }
  
  # Main root part
  temp2$MRdouble <- "no"
  for(i in 2:nrow(temp2)){
   if(temp2$MRL[i] == temp2$MRL[i-1]){
    temp2$MRdouble[i] <- "yes"   
   } 
    else{
    temp2$MRdouble[i] <- "no"   
   } 
  }
  
  temp2
  # Main Root Growth Factor calculations: 
  temp3 <- subset(temp2, temp2$MRdouble == "no")
  model <- lm(temp3$MRL ~ temp3$DAS)
  growth_factors[e,1] <- temp3$root_name[1]
  growth_factors[e,2] <- temp3$genotype[1]
  growth_factors[e,3] <- temp3$cond[1]
  growth_factors[e,4] <- as.numeric(as.character(model$coefficients[[1]]))
  growth_factors[e,5] <- as.numeric(as.character(model$coefficients[[2]]))
  
 
}
```


```{r}
dim(growth_factors)
```

Great - let's calculate the LRno increase rate - with some special conditions for the lines which don't have any LR:

```{r}
for(e in 1:length(unique(all_MR3$root_name))){
  temp1 <- subset(all_MR3, all_MR3$root_name == unique(all_MR3$root_name)[e])
  temp2 <- temp1[order(temp1$DAS),]

 # Let's remove all NA for LRno and aLRL in temp2
  super_temp <- temp2[,c("DAS", "LRno", "aLRL")]
  temp2 <- na.omit(super_temp)
  
  if(dim(temp2)[1] > 1){
    model <- lm(temp2$LRno ~ temp2$DAS)
    growth_factors[e,6] <- as.numeric(as.character(model$coefficients[[1]]))
    growth_factors[e,7] <- as.numeric(as.character(model$coefficients[[2]]))  
  }
  # Lateral Root Number Increase calculations:
  else{
    growth_factors[e,6] <- 0
    growth_factors[e,7] <- 0
  }
}  
```

Same thing for aLRL:

```{r}
for(e in 1:length(unique(all_MR3$root_name))){
  temp1 <- subset(all_MR3, all_MR3$root_name == unique(all_MR3$root_name)[e])
  temp2 <- temp1[order(temp1$DAS),]

 # Let's remove all NA for LRno and aLRL in temp2
  super_temp <- temp2[,c("DAS", "LRno", "aLRL")]
  temp2 <- na.omit(super_temp)
  temp2
  dim(temp2)[1]
  if(dim(temp2)[1] > 1){
    model <- lm(temp2$aLRL ~ temp2$DAS)
    model
    growth_factors[e,8] <- as.numeric(as.character(model$coefficients[[1]]))
    growth_factors[e,9] <- as.numeric(as.character(model$coefficients[[2]]))  
  }
  # Lateral Root Number Increase calculations:
  else{
    growth_factors[e,8] <- 0
    growth_factors[e,9] <- 0
  }
}  
```

Let's have a look at the growth_factors table:

```{r}
growth_factors
min(growth_factors$MR.delta)
min(growth_factors$LRno.delta)
min(growth_factors$aLRL.delta)

max(growth_factors$MR.delta)
max(growth_factors$LRno.delta)
max(growth_factors$aLRL.delta)

sapply(growth_factors, class)
growth_factors$MR.delta <- as.numeric(growth_factors$MR.delta)
growth_factors$LRno.delta <- as.numeric(growth_factors$LRno.delta)
growth_factors$aLRL.delta <- as.numeric(growth_factors$aLRL.delta)
```

## Histograms for growth rates

Most values seem to be in the reasonable range - but let's have a look at the histogram

```{r}
MR.delta_histo <- gghistogram(growth_factors, x = "MR.delta",
            add = "mean", rug = TRUE,
            color = "cond", fill = "cond", 
            palette = c("#00AFBB", "#E7B800")) + xlab("Main Root Growth (cm / day)")
MR.delta_histo
```

```{r}
LRno.delta_histo <- gghistogram(growth_factors, x = "LRno.delta",
            add = "mean", rug = TRUE,
            color = "cond", fill = "cond", 
            palette = c("#00AFBB", "#E7B800")) + xlab("Increase in Lateral Root number (# LR / day)")
LRno.delta_histo
```

```{r}
aLRL.delta_histo <- gghistogram(growth_factors, x = "aLRL.delta",
            add = "mean", rug = TRUE,
            color = "cond", fill = "cond", 
            palette = c("#00AFBB", "#E7B800")) + xlab("average Lateral Root Growth (cm / day)")
aLRL.delta_histo
```

awesome - let's save this into one figure as well:

```{r}
pdf("Fig.Growth_rates_histograms_MR_noLR_aLRL.pdf", width = 7.5, height = 10)
plot_grid(MR.delta_histo, LRno.delta_histo, aLRL.delta_histo, ncol=1, labels = "AUTO")
dev.off()
```

## reproducibility within accessions for growth rates:

let's have a look what is the diversity of growth rates within accessions:

```{r}
grand.means <- aggregate(MR.delta ~ cond, data = growth_factors, FUN = mean)
grand.means

unique(growth_factors$genotype)

MR.delta_p_geno <- ggerrorplot(growth_factors, y = "MR.delta", x = "genotype", fill="genotype", color="genotype", 
                        facet.by = c("cond"), ncol=1,
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="Genotype", ylab="Main Root Growth (cm / day)") 
MR.delta_p_geno <- MR.delta_p_geno + geom_hline(
                          data = grand.means, aes(yintercept = MR.delta),
                          linetype = 2,
                          group = "cond")
MR.delta_p_geno <- MR.delta_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = ".all.", 
                                                              label = "p.signif", hide.ns = T)
MR.delta_p_geno
```
```{r}
grand.means <- aggregate(LRno.delta ~ cond, data = growth_factors, FUN = mean)
grand.means

LRno.delta_p_geno <- ggerrorplot(growth_factors, y = "LRno.delta", x = "genotype", fill="genotype", color="genotype", 
                        facet.by = c("cond"), ncol=1,
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="Genotype", ylab="Increase in LR number (# LR / day)") 
LRno.delta_p_geno <- LRno.delta_p_geno + geom_hline(
                          data = grand.means, aes(yintercept = LRno.delta),
                          linetype = 2,
                          group = "cond")
LRno.delta_p_geno <- LRno.delta_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = ".all.", 
                                                              label = "p.signif", hide.ns = T)
LRno.delta_p_geno
```

```{r}
grand.means <- aggregate(aLRL.delta ~ cond, data = growth_factors, FUN = mean)
grand.means

aLRL.delta_p_geno <- ggerrorplot(growth_factors, y = "aLRL.delta", x = "genotype", fill="genotype", color="genotype", 
                        facet.by = c("cond"), ncol=1,
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="Genotype", ylab="average Lateral Root Growth (cm / day)") 
aLRL.delta_p_geno <- aLRL.delta_p_geno + geom_hline(
                          data = grand.means, aes(yintercept = aLRL.delta),
                          linetype = 2,
                          group = "cond")
aLRL.delta_p_geno <- aLRL.delta_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = ".all.", 
                                                              label = "p.signif", hide.ns = T)
aLRL.delta_p_geno
```

ok - but these trends are very general - let's have a look what is the diversity between accessions in their salt responses. 

## stress-induced changes in growth rates:

Also - maybe lets calculate the average growth rate per accession and look how this looks on a graph with some stats - as in - do we see sig. differences between C and S:

```{r}
head(growth_factors)
growth_nona <- na.omit(growth_factors)
sum_growth <- summaryBy(data = growth_factors, MR.delta + LRno.delta + aLRL.delta ~ cond + genotype)
sum_growth

MR.delta_dotplot <- ggerrorplot(sum_growth, y = "MR.delta.mean", x = "cond", fill="cond", color="cond", 
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="Condition", ylab="Main Root Growth (cm / day)") 
MR.delta_dotplot <- MR.delta_dotplot + rremove("legend") + stat_compare_means(method="t.test", ref.group = "C", 
                                                              label = "p.signif", hide.ns = T)
MR.delta_dotplot
```


```{r}
aLRL.delta_dotplot <- ggerrorplot(sum_growth, y = "aLRL.delta.mean", x = "cond", fill="cond", color="cond", 
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="Condition", ylab="average Lateral Root Growth (cm / day)") 
aLRL.delta_dotplot <- aLRL.delta_dotplot + rremove("legend") + stat_compare_means(method="t.test", ref.group = "C", 
                                                              label = "p.signif", hide.ns = T)
aLRL.delta_dotplot
```

```{r}
LRno.delta_dotplot <- ggerrorplot(sum_growth, y = "LRno.delta.mean", x = "cond", fill="cond", color="cond", 
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="Condition", ylab="average Lateral Root Growth (cm / day)") 
LRno.delta_dotplot <- LRno.delta_dotplot + rremove("legend") + stat_compare_means(method="t.test", ref.group = "C", 
                                                              label = "p.signif", hide.ns = T)
LRno.delta_dotplot
```

ok - now we have all the accession values averaged - let's compare the growth rate under control and salt stress

First - we need to split the data into control and salt and then match them based on genotype:

```{r}
growth_C <- subset(sum_growth, sum_growth$cond == "C")
growth_S <- subset(sum_growth, sum_growth$cond == "S")
colnames(growth_C) <- gsub(".mean", ".Control", colnames(growth_C))
colnames(growth_S) <- gsub(".mean", ".Salt", colnames(growth_S))

growth_C2 <- growth_C[,c(2:5)]
growth_S2 <- growth_S[,c(2:5)]

sum_growth2 <- merge(growth_C2, growth_S2, by="genotype")
sum_growth2
```

```{r}
corrplot_MR_gr <- ggscatter(sum_growth2, x = "MR.delta.Control", y = "MR.delta.Salt", size = 0.3, 
                            rug = TRUE, add = "reg.line", color = "LRno.delta.Control") + stat_cor(method = "pearson")
corrplot_MR_gr <- corrplot_MR_gr + gradient_color(c("blue", "grey", "red"))
corrplot_MR_gr
```

```{r}
corrplot_aLRL_gr <- ggscatter(sum_growth2, x = "aLRL.delta.Control", y = "aLRL.delta.Salt", size = 0.3, 
                            rug = TRUE, add = "reg.line", color = "aLRL.delta.Control") + stat_cor(method = "pearson")
corrplot_aLRL_gr <- corrplot_aLRL_gr + gradient_color(c("blue", "grey", "red"))
corrplot_aLRL_gr


```

```{r}
corrplot_LRno_gr <- ggscatter(sum_growth2, x = "LRno.delta.Control", y = "LRno.delta.Salt", color = "LRno.delta.Control",
                              size = 0.3, rug = TRUE, add = "reg.line") + stat_cor(method = "pearson")
corrplot_LRno_gr <- corrplot_LRno_gr + gradient_color(c("blue", "grey", "red"))
corrplot_LRno_gr
```

hmmm... thinking about the dotplots from earlier and they *should* actually be a *paired* graph, because we want to see how individual accessions are behaving, and whether that *behaviour* is conserved across accessions:

```{r}
MR_sum_growth <- sum_growth2[,c(1,2,5)]
MR_sum_melt <- melt(MR_sum_growth)
MR_sum_melt
MR_sum_melt$variable <- gsub("MR.delta.", "", MR_sum_melt$variable)
colnames(MR_sum_melt)[2] <- "condition"
colnames(MR_sum_melt)[3] <- "MR.delta"


MR_growth_paired <- ggpaired(MR_sum_melt, x = "condition", y = "MR.delta",
                               color = "condition", line.color = "gray", line.size = 0.1,
                               palette = "jco", xlab ="", ylab ="Main Root Growth (cm / day)")
MR_growth_paired <- MR_growth_paired + stat_compare_means(paired = TRUE, label.x = 1.5) + rremove("legend")
MR_growth_paired
```

Cool - that looks much more informative - let's do this for other traits:

```{r}
LRno_sum_growth <- sum_growth2[,c(1,3,6)]
LRno_sum_growth <- na.omit(LRno_sum_growth)
LRno_sum_melt <- melt(LRno_sum_growth)
LRno_sum_melt
LRno_sum_melt$variable <- gsub("LRno.delta.", "", LRno_sum_melt$variable)
colnames(LRno_sum_melt)[2] <- "condition"
colnames(LRno_sum_melt)[3] <- "LRno.delta"


LRno_growth_paired <- ggpaired(LRno_sum_melt, x = "condition", y = "LRno.delta",
                               color = "condition", line.color = "gray", line.size = 0.1,
                               palette = "jco", xlab ="", ylab ="Increase in Lateral Root # (# LR / day)")
LRno_growth_paired <- LRno_growth_paired + stat_compare_means(paired = TRUE, label.x = 1.5) + rremove("legend")
LRno_growth_paired
```

```{r}
aLRL_sum_growth <- sum_growth2[,c(1,4,7)]
aLRL_sum_growth <- na.omit(aLRL_sum_growth)
aLRL_sum_melt <- melt(aLRL_sum_growth)
aLRL_sum_melt
aLRL_sum_melt$variable <- gsub("aLRL.delta.", "", aLRL_sum_melt$variable)
colnames(aLRL_sum_melt)[2] <- "condition"
colnames(aLRL_sum_melt)[3] <- "aLRL.delta"


aLRL_growth_paired <- ggpaired(aLRL_sum_melt, x = "condition", y = "aLRL.delta",
                               color = "condition", line.color = "gray", line.size = 0.1,
                               palette = "jco", xlab ="", ylab ="average Lateral Root Growth (cm / day)")
aLRL_growth_paired <- aLRL_growth_paired + stat_compare_means(paired = TRUE, label.x = 1.5) + rremove("legend")
aLRL_growth_paired
```

ok - now let's start comparing how individual accessions are doing when it comes to salt indices of individual organs:

## Salt Tolerance Indexes

In order to look at salt responses - we need to calculate the average plant responses per accession and calculate the relative change in growth rate in response to salt (relative to control):

```{r}
head(sum_growth2)

sum_growth2$MR.STI <- sum_growth2$MR.delta.Salt/sum_growth2$MR.delta.Control
sum_growth2$LRno.STI <- sum_growth2$LRno.delta.Salt/sum_growth2$LRno.delta.Control
sum_growth2$aLRL.STI <- sum_growth2$aLRL.delta.Salt/sum_growth2$aLRL.delta.Control
head(sum_growth2)
```

Then - we would like to know also what is the effect of the salt on different root components across different accessions - so let's make a histogram for this :)

```{r}
colnames(sum_growth2)
STI_histo_set <- sum_growth2[,c(1,8:10)]
head(STI_histo_set)
STI_histo_melt <- melt(STI_histo_set)
head(STI_histo_melt)

STI_growth_histo <- gghistogram(STI_histo_melt, x = "value",
            add = "mean", rug = TRUE,
            color = "variable", fill = "variable", 
            palette = c("#1b9e77", "#d95f02", "#7570b3")) + xlab("fraction of growth at control")
STI_growth_histo
```

cool - now we can also look in detail of how the individual accessions behave throughout these three STI

```{r}
STI_growth_paired <- ggpaired(STI_histo_melt, x = "variable", y = "value",
                               color = "variable", line.color = "gray", line.size = 0.1,
                               palette = "jco", xlab ="", ylab ="Fraction of rate at Control")
STI_growth_paired <- STI_growth_paired + stat_compare_means(paired = TRUE, label.x = 1.5) + rremove("legend")
STI_growth_paired
```

ok - also - lets look at these things in a bit more traditional setting:

```{r}
sum_growth2
STI_MR_vs_aLRL <- ggscatter(sum_growth2, x = "MR.STI", y = "aLRL.STI", color = "aLRL.delta.Control", size = 0.3, 
                            rug = TRUE, add = "reg.line") + stat_cor(method = "pearson")
STI_MR_vs_aLRL <- STI_MR_vs_aLRL + gradient_color(c("blue", "grey", "red"))

STI_MR_vs_aLRL
```

```{r}
STI_MR_vs_LRno <- ggscatter(sum_growth2, x = "MR.STI", y = "LRno.STI", color = "LRno.delta.Control", size = 0.3, 
                            rug = TRUE, add = "reg.line") + stat_cor(method = "pearson")
STI_MR_vs_LRno <- STI_MR_vs_LRno + gradient_color(c("blue", "grey", "red"))

STI_MR_vs_LRno
```

```{r}
STI_aLRL_vs_LRno <- ggscatter(sum_growth2, x = "aLRL.STI", y = "LRno.STI", color = "LRno.delta.Control", size = 0.3, 
                            rug = TRUE, add = "reg.line") + stat_cor(method = "pearson")
STI_aLRL_vs_LRno <- STI_aLRL_vs_LRno + gradient_color(c("blue", "grey", "red"))

STI_aLRL_vs_LRno
```

## Conclusions so far

OK - so all of the above is pretty interesting - from the paired STI graph we can tell that there are multiple classes of accessions:

```{r}
STI_growth_paired
```

1) accessions that are equally impaired for all components of RSA
2) accessions that are LEAST impaired in MRL
3) accessions that are MOST impaired in MRL
4) accessions that are MOST impaired in LRno

The correlations between individual SITs is very low for all combinations - which is pretty exciting, and aLRL and MRL are in general the least impacted by the salt stress - while LRno is impaired in most accessions the most - based on this histogram:

```{r}
STI_growth_histo
```

now - I am wondering - if we throw only the STI into the hierarchical clustering - would we be able to discern between these classes of accessions?

Let's see:

## Hclust based on the SITs:

First - let's prepare the matrix:

```{r}
colnames(sum_growth2)
STI_only <- sum_growth2[,c(1,8:10)]
# get only numeric variables from your data - NOT plant identifiers 
STI_matrix <- STI_only[, c(2:4)]
# transfer all numeric variables into matrix
STI_matrix2 <- as.matrix(STI_matrix)
# add plant identifiers as row names
row.names(STI_matrix2) <- STI_only$genotype
# omit missing values - otherwise we wont be able to do the clustering 
final_m <- na.omit(STI_matrix2)
dim(STI_matrix2)
dim(final_m)
head(final_m)
```

```{r}
# run a correlation
magda_cor = cor(final_m,method=c("pearson"))
# calculate distance
magda_dist = dist(magda_cor)
# this is the clustering itself - but now we are clustering how phenotypes are related to eachother
magda_clust = hclust(magda_dist,method="ward.D2")
plot(as.dendrogram(magda_clust),horiz=T)
# transposing the matrix
magda_t_matrix=t(final_m)
magda_t_cor = cor(magda_t_matrix,method=c("pearson"))
magda_t_dist = dist(magda_t_cor)
# clustering of accessions
magda_t_clust = hclust(magda_t_dist,method="ward.D2")
plot(as.dendrogram(magda_t_clust),horiz=T)
clusters <- cutree(magda_t_clust,h=24)
unique(clusters)
write.table(as.data.frame(cutree(magda_t_clust,h=24)),file="STI_clusters.txt",sep="\t",quote=F)
# the scaling only now applies to "row" which are our different phenotypes
heatmap.2(magda_t_matrix,Rowv=as.dendrogram(magda_clust),Colv=as.dendrogram(magda_t_clust),col=blue2red(100),scale=c("row"),density.info="none",trace="none")
```

OK - let's save this one into a file before we forget

```{r}
pdf("Fig.STI_clustering.pdf",width=12,height=7)
heatmap.2(magda_t_matrix,Rowv=as.dendrogram(magda_clust),Colv=as.dendrogram(magda_t_clust),col=blue2red(100),scale=c("row"),density.info="none",trace="none")
dev.off()
```

cool - now let's see if the clusters actually differ in their respective growth rates / STIs.

First - let's fuse "clusters" with the other growth rate data
```{r}
clusters

sumatrix <- sum_growth2[,c(2:10)]
sumatrix2 <- as.matrix(sumatrix)
row.names(sumatrix2) <- sum_growth2$genotype
sumatrix3 <- na.omit(sumatrix2)

sum_growth3 <- cbind(sumatrix3, clusters)
head(sum_growth3)
```

ok - now let's make some graphs per clusters starting with STIs:

```{r}
growth_cluster3 <- as.data.frame(sum_growth3)
growth_cluster3

STIcluster_MR.STI <- ggerrorplot(growth_cluster3, y = "MR.STI", x = "clusters", fill="clusters", 
                                   color="clusters", desc_stat = "mean_sd", add = "jitter", 
                                   add.params = list(color = "darkgray"), xlab="", 
                                   ylab="Fraction of Control (Main Root Growth)", title = "Main Root STI") 
STIcluster_MR.STI <- STIcluster_MR.STI + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1, 
                                                              label = "p.signif", hide.ns = T)
STIcluster_MR.STI
```

```{r}
STIcluster_LRno.STI <- ggerrorplot(growth_cluster3, y = "LRno.STI", x = "clusters", fill="clusters", 
                                   color="clusters", desc_stat = "mean_sd", add = "jitter", 
                                   add.params = list(color = "darkgray"), xlab="", 
                                   ylab="Fraction of Control (LR #)", title = "Lateral Root # STI") 
STIcluster_LRno.STI <- STIcluster_LRno.STI + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1, 
                                                              label = "p.signif", hide.ns = T)
STIcluster_LRno.STI
```

```{r}
STIcluster_aLRL.STI <- ggerrorplot(growth_cluster3, y = "aLRL.STI", x = "clusters", fill="clusters", 
                                   color="clusters", desc_stat = "mean_sd", add = "jitter", 
                                   add.params = list(color = "darkgray"), xlab="", 
                                   ylab="Fraction of Control (avg. Lateral Root Growth)", 
                                   title = " avg Lateral Root Growth STI") 
STIcluster_aLRL.STI <- STIcluster_aLRL.STI + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1, 
                                                              label = "p.signif", hide.ns = T)
STIcluster_aLRL.STI
```

Let's put these into one figure and think about it in another session:

```{r}
pdf("Fig.STI_based_clusters_STI_trait_comparison.pdf", height = 15, width = 10)
plot_grid(STIcluster_MR.STI, STIcluster_LRno.STI, STIcluster_aLRL.STI, ncol=1, labels="AUTO")
dev.off()
```

I m still wondering how the correlations between the traits would look if we would color it per cluster now:

```{r}
growth_cluster3$clusters <- as.factor(growth_cluster3$clusters)

STI_aLRL_vs_LRno_clust <- ggscatter(growth_cluster3, x = "aLRL.STI", y = "LRno.STI", color = "clusters", size = 0.3, 
                            rug = TRUE, ellipse = TRUE, palette = "jco") + stat_cor(method = "pearson")
STI_aLRL_vs_LRno_clust
```

```{r}
STI_aLRL_vs_MR_clust <- ggscatter(growth_cluster3, x = "aLRL.STI", y = "MR.STI", color = "clusters", size = 0.3, 
                            rug = TRUE, ellipse = TRUE, palette = "jco") + stat_cor(method = "pearson")
STI_aLRL_vs_MR_clust
```

```{r}
STI_LRno_vs_MR_clust <- ggscatter(growth_cluster3, x = "LRno.STI", y = "MR.STI", color = "clusters", size = 0.3, 
                            rug = TRUE, ellipse = TRUE, palette = "jco") + stat_cor(method = "pearson")
STI_LRno_vs_MR_clust
```

I wonder if we could visualize accessions that belong to individual clusters in the continuous graphs with average per accession.

Let's load the STI_cluster table:
```{r}
cluster01 <- read.table("STI_clusters_better.txt", header = T)
head(cluster01)
```

now let's fuse it with the MR_sum dataset that we used previously for timeseries graphs:

```{r}
head(MR_sum)
MR_sum2 <- MR_sum
MR_sum2$genotype <- as.numeric(as.character(MR_sum2$genotype))
MR_cluster <- merge(MR_sum2, cluster01, by="genotype", all=T)
head(MR_cluster)
MR_cluster$cluster <- as.factor(MR_cluster$cluster)
```

Cool - now let's see how the TRS graph looks like when we colour per cluster:

```{r}
TRS_clust_lgraph <- ggplot(data=MR_cluster, aes(x= DAS, y=TRS.mean, group = geno_cond, color = cond)) 
TRS_clust_lgraph <- TRS_clust_lgraph + geom_line(alpha = 0.1) 
TRS_clust_lgraph <- TRS_clust_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3) + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
TRS_clust_lgraph <- TRS_clust_lgraph + ggtitle("") +facet_grid(~ cluster)
TRS_clust_lgraph <- TRS_clust_lgraph + ylab("Total Root Size (cm)") + xlab("Days After Stress")
TRS_clust_lgraph
```



```{r}
STIcluster_MR.C <- ggerrorplot(growth_cluster3, y = "MR.delta.Control", x = "clusters", fill="clusters", 
                                   color="clusters", desc_stat = "mean_sd", add = "jitter", 
                                   add.params = list(color = "darkgray"), xlab="Clusters based on STI", 
                                   ylab="Main Root Growth (cm / day)", title = "Control") 
STIcluster_MR.C <- STIcluster_MR.C + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1, 
                                                              label = "p.signif", hide.ns = T)
STIcluster_MR.C
```

```{r}
STIcluster_MR.S <- ggerrorplot(growth_cluster3, y = "MR.delta.Salt", x = "clusters", fill="clusters", 
                                   color="clusters", desc_stat = "mean_sd", add = "jitter", 
                                   add.params = list(color = "darkgray"), xlab="Clusters based on STI", 
                                   ylab="Main Root Growth (cm / day)", title = "Salt") 
STIcluster_MR.S <- STIcluster_MR.S + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1, 
                                                              label = "p.signif", hide.ns = T)
STIcluster_MR.S
```

```{r}
STIcluster_aLR.C <- ggerrorplot(growth_cluster3, y = "aLRL.delta.Control", x = "clusters", fill="clusters", 
                                   color="clusters", desc_stat = "mean_sd", add = "jitter", 
                                   add.params = list(color = "darkgray"), xlab="Clusters based on STI", 
                                   ylab="avg Lateral Root Growth (cm / day)", title = "Control") 
STIcluster_aLR.C <- STIcluster_aLR.C + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1, 
                                                              label = "p.signif", hide.ns = T)
STIcluster_aLR.C
```

```{r}
STIcluster_aLR.S <- ggerrorplot(growth_cluster3, y = "aLRL.delta.Salt", x = "clusters", fill="clusters", 
                                   color="clusters", desc_stat = "mean_sd", add = "jitter", 
                                   add.params = list(color = "darkgray"), xlab="Clusters based on STI", 
                                   ylab="avg Lateral Root Growth (cm / day)", title = "Salt") 
STIcluster_aLR.S <- STIcluster_aLR.S + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1, 
                                                              label = "p.signif", hide.ns = T)
STIcluster_aLR.S
```

```{r}
STIcluster_LRno.C <- ggerrorplot(growth_cluster3, y = "LRno.delta.Control", x = "clusters", fill="clusters"  
                                   color="clusters", desc_stat = "mean_sd", add = "jitter", 
                                   add.params = list(color = "darkgray"), xlab="Clusters based on STI", 
                                   ylab="Lateral Root Increase (# LR / day)", title = "Control") 
STIcluster_LRno.C <- STIcluster_LRno.C + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1, 
                                                              label = "p.signif", hide.ns = T)
STIcluster_LRno.C
library(plotly)
ggplotly(STIcluster_LRno.C)
```


```{r}
STIcluster_LRno.S <- ggerrorplot(growth_cluster3, y = "LRno.delta.Salt", x = "clusters", fill="clusters", 
                                   color="clusters", desc_stat = "mean_sd", add = "jitter", 
                                   add.params = list(color = "darkgray"), xlab="Clusters based on STI", 
                                   ylab="Lateral Root Increase (# LR / day)", title = "Salt") 
STIcluster_LRno.S <- STIcluster_LRno.S + rremove("legend") + stat_compare_means(method="t.test", ref.group = 1, 
                                                              label = "p.signif", hide.ns = T)
STIcluster_LRno.S
```

```{r}
ggplotly(TRS_lgraph)
```

Let's have a look and select accessions:

```{r}
tomato <- subset(MR_sum, MR_sum$DAS > 3)
tomato <- subset(tomato, tomato$cond == "S")
tomato <- subset(tomato, tomato$TRS.mean > 19)
tomato$genotype

```

that's of course based on the mean - lets have a look at the most interesting accessions tho:

```{r}
interesting <- subset(all_MR3, all_MR3$genotype == " 002")
interesting

M002_lgraph <- ggplot(data=interesting, aes(x= DAS, y=TRS, group = root_name, color = cond)) 
M002_lgraph <- M002_lgraph +geom_line(alpha = 0.1) 
M002_lgraph <- M002_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
M002_lgraph <- M002_lgraph + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
M002_lgraph <- M002_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
M002_lgraph <- M002_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("M002")
M002_lgraph <- M002_lgraph + ylab("Total Root Size (cm)") + xlab("Days After Stress") + theme(legend.position='none')
M002_lgraph
```


```{r}
interesting <- subset(all_MR3, all_MR3$genotype == " 248")
interesting

M248_lgraph <- ggplot(data=interesting, aes(x= DAS, y=TRS, group = root_name, color = cond)) 
M248_lgraph <- M248_lgraph +geom_line(alpha = 0.1) 
M248_lgraph <- M248_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
M248_lgraph <- M248_lgraph + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
M248_lgraph <- M248_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
M248_lgraph <- M248_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("M248")
M248_lgraph <- M248_lgraph + ylab("Total Root Size (cm)") + xlab("Days After Stress") + theme(legend.position='none')
M248_lgraph
```

## Pareto and RSA

OK - so now let's get to the actual paretho calculations made by Arjun.

```{r}
getwd()
Pareto_01 <- read.csv("/Users/magdalena/Dropbox/DataAndAnalysis/Tomato/PimpiBIG csv raw files/Arjun_Paretho/arbor_stats.csv")
Pareto_01
Pareto_02 <- read.csv("/Users/magdalena/Dropbox/DataAndAnalysis/Tomato/PimpiBIG csv raw files/Arjun_Paretho/scaling_distances.csv")
Pareto_02
head(all_MR3)
meta <- read.csv("/Users/magdalena/Dropbox/DataAndAnalysis/Tomato/PimpiBIG csv raw files/Arjun_Paretho/metadata.csv")
meta
```

Cool - let's try to fuse it all together:

```{r}
dim(Pareto_01)
dim(Pareto_02)
dim(meta)
dim(all_MR3)
```

ok - so for some reason we do have less paretho calculated than actually plants, but that's ok at this point. 

Let's first fuse two paretho files:

```{r}
Pareto_all <- merge(Pareto_01, Pareto_02, by="arbor.name")
dim(Pareto_all)
head(Pareto_all)
```

Then add meta-data

```{r}
unique(Pareto_all$arbor.name)
unique(meta$arbor.name)

Pareto_all$arbor.name <- gsub(" ", "", Pareto_all$arbor.name)
meta$arbor.name <- gsub(" ", "", meta$arbor.name)

Pareto_meta <- merge(Pareto_all, meta, by="arbor.name")
Pareto_meta
dim(Pareto_meta)
```

Let's clean up this pareto_meta file:

```{r}
colnames(Pareto_meta)
Pareto <- Pareto_meta[,c(8:13, 1:5)]
dim(Pareto)
colnames(Pareto)[11] <- "pareto.front.scaling.location"
Pareto
```

Now - let's try and connect it to the MR_all3 file:

```{r}
all_MR3
unique(all_MR3$genotype)
all_MR3$arbor.name <- paste(all_MR3$genotype, "_", all_MR3$rep, "_", all_MR3$cond, "_DAS", all_MR3$DAS, sep="")
head(all_MR3)

all_MR3$arbor.name <- gsub("DAS0", "day1", all_MR3$arbor.name)
all_MR3$arbor.name <- gsub("DAS1", "day2", all_MR3$arbor.name)
all_MR3$arbor.name <- gsub("DAS2", "day3", all_MR3$arbor.name)
all_MR3$arbor.name <- gsub("DAS3", "day4", all_MR3$arbor.name)
all_MR3$arbor.name <- gsub("DAS4", "day5", all_MR3$arbor.name)
all_MR3$arbor.name <- gsub(" ", "", all_MR3$arbor.name)
head(all_MR3)
unique(all_MR3$arbor.name) %in% unique(Pareto$arbor.name)
all_MR4 <- merge(all_MR3, Pareto, by="arbor.name")
dim(all_MR4)
all_MR4
```

Let's clean it up a little before going further:

```{r}
colnames(all_MR4)

all_MR4 <- all_MR4[,c(1:65,72:75)]
colnames(all_MR4)[5] <- "experiment"
colnames(all_MR4)[7] <- "genotype"
all_MR4
```

### Pareto through time:

Now we have all the data - let's have a look at how that is looking through time:

```{r}
all_MR5 <- na.omit(all_MR4)
all_MR5$DAS <- as.factor(all_MR5$DAS)

PFdist_lgraph <- ggplot(data=all_MR5, aes(x= DAS, y=pareto.front.distance, group = root_name, color = cond)) 
PFdist_lgraph <- PFdist_lgraph +geom_line(alpha = 0.1) 
PFdistance_lgraph <- PFdistance_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
PFdistance_lgraph <- PFdistance_lgraph + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
PFdistance_lgraph <- PFdistance_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)

PFdistance_lgraph <- PFdistance_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Individual accessions")
PFdistance_lgraph <- PFdistance_lgraph + ylab("Pareto Front Distance (a.u.)") + xlab("Days After Stress") + theme(legend.position='none')
PFdistance_lgraph
```

```{r}
PFscdist_lgraph <- ggplot(data=all_MR5, aes(x= DAS, y=pareto.front.scaling.distance, group = root_name, color = cond)) 
PFscdist_lgraph <- PFscdist_lgraph +geom_line(alpha = 0.1) 
PFscdist_lgraph <- PFscdist_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
PFscdist_lgraph <- PFscdist_lgraph + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
PFscdist_lgraph <- PFscdist_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)

PFscdist_lgraph <- PFscdist_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Individual accessions")
PFscdist_lgraph <- PFscdist_lgraph + ylab("Pareto Front Scaling Distance (a.u.)") + xlab("Days After Stress") + theme(legend.position='none')
PFscdist_lgraph
```


```{r}
PFloc_lgraph <- ggplot(data=all_MR5, aes(x= DAS, y=pareto.front.location, group = root_name, color = cond)) 
PFloc_lgraph <- PFloc_lgraph +geom_line(alpha = 0.1) 
PFloc_lgraph <- PFloc_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
PFloc_lgraph <- PFloc_lgraph + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
PFloc_lgraph <- PFloc_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
PFloc_lgraph <- PFloc_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Individual accessions")
PFloc_lgraph <- PFloc_lgraph + ylab("Pareto Front Location (a.u.)") + xlab("Days After Stress") + theme(legend.position='none')
PFloc_lgraph
```

```{r}
PFscloc_lgraph <- ggplot(data=all_MR5, aes(x= DAS, y=pareto.front.scaling.location, group = root_name, color = cond)) 
PFscloc_lgraph <- PFscloc_lgraph +geom_line(alpha = 0.1) 
PFscloc_lgraph <- PFscloc_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
PFscloc_lgraph <- PFscloc_lgraph + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
PFscloc_lgraph <- PFscloc_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
PFscloc_lgraph <- PFscloc_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Individual accessions")
PFscloc_lgraph <- PFscloc_lgraph + ylab("Pareto Front Scaling Location (a.u.)") + xlab("Days After Stress") + theme(legend.position='none')
PFscloc_lgraph
```

ok - now let's have a look what is the reproducibility of the pareto front location within the genotype:

```{r}
interesting <- subset(all_MR5, all_MR5$genotype == " 248")
interesting

M248_PFScloc <- ggplot(data=interesting, aes(x= DAS, y=pareto.front.scaling.location, group = root_name, color = cond)) 
M248_PFScloc <- M248_PFScloc +geom_line(alpha = 0.2) 
M248_PFScloc <- M248_PFScloc + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
M248_PFScloc <- M248_PFScloc + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
M248_PFScloc <- M248_PFScloc + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
M248_PFScloc <- M248_PFScloc + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("M248")
M248_PFScloc <- M248_PFScloc + ylab("Pareto Front Scaling Location") + xlab("Days After Stress") + theme(legend.position='none')
M248_PFScloc
```

Let's save this file and explore the heritability of these traits in MVApp:

```{r}
write.csv(all_MR5, "Big01to04_incl.PF.csv", row.names = FALSE)
```

The heritabiity of all the traits is pretty decent overall - between 0.4 and 0.7 in most cases. 

Let's see how the Pareto Front Scaling Location is correlated with LR traits:

```{r}
all_MR5$DAS <- as.factor(all_MR5$DAS)
PFsc.loc_vs_TRS <- ggscatter(all_MR5, x = "pareto.front.scaling.location", y = "TRS", color = "cond", size = 0.3, 
                            rug = TRUE, facet.by = "DAS") + stat_cor(method = "pearson")
PFsc.loc_vs_TRS
```

```{r}
PFsc.loc_vs_LRno <- ggscatter(all_MR5, x = "pareto.front.scaling.location", y = "LRno", color = "cond", size = 0.3, 
                            rug = TRUE, facet.by = "DAS") + stat_cor(method = "pearson")
PFsc.loc_vs_LRno
```


```{r}
PFsc.loc_vs_aLRL <- ggscatter(all_MR5, x = "pareto.front.scaling.location", y = "aLRL", color = "cond", size = 0.3, 
                            rug = TRUE, facet.by = "DAS") + stat_cor(method = "pearson")
PFsc.loc_vs_aLRL
```

```{r}
PFsc.loc_vs_CoG <- ggscatter(all_MR5, x = "pareto.front.scaling.location", y = "CoG", color = "cond", size = 0.3, 
                            rug = TRUE, facet.by = "DAS") + stat_cor(method = "pearson")
PFsc.loc_vs_CoG
```

actually - CoG should be calculated as perc. or MR - I wonder why I didnt do that before :)

```{r}
colnames(all_MR5)
all_MR5$CoG.per <- all_MR5$CoG / all_MR5$MRL

CoGperc_lgraph <- ggplot(data=all_MR5, aes(x= DAS, y=CoG.per, group = root_name, color = cond)) 
CoGperc_lgraph <- CoGperc_lgraph +geom_line(alpha = 0.1) 
CoGperc_lgraph <- CoGperc_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=cond), alpha=0.3)
CoGperc_lgraph <- CoGperc_lgraph + stat_summary(fun.y=mean, aes(group= cond),  size=0.7, geom="line", linetype = "dashed")
CoGperc_lgraph <- CoGperc_lgraph + stat_compare_means(aes(group = cond), label = "p.signif", method = "t.test", hide.ns = T)
CoGperc_lgraph <- CoGperc_lgraph + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("Individual accessions")
CoGperc_lgraph <- CoGperc_lgraph + ylab("Fraction of the Main Root") + xlab("Days After Stress") + theme(legend.position='none')
CoGperc_lgraph

PFsc.loc_vs_CoG.perc <- ggscatter(all_MR5, x = "pareto.front.scaling.location", y = "CoG.per", color = "cond", size = 0.3, 
                            rug = TRUE, facet.by = "DAS") + stat_cor(method = "pearson")
PFsc.loc_vs_CoG.perc
```

Also - I would like to have a look at how this scaling.location looks across the accessions within one day and between conditions - lets do DAS4

```{r}
DAS4 <- subset(all_MR5, all_MR5$DAS == 4)

grand.means <- aggregate(pareto.front.scaling.location ~ cond, data = DAS4, FUN = mean)
grand.means

unique(DAS4$genotype)

PF.sc.loc_p_geno <- ggerrorplot(DAS4, y = "pareto.front.scaling.location", x = "genotype", fill="genotype",
                               color="genotype", facet.by = c("cond"), ncol=1, desc_stat = "mean_sd", add = "jitter", 
                               add.params = list(color = "darkgray"), xlab="Genotype", 
                               ylab="Pareto Front Scaling Location (a.u.)") 
PF.sc.loc_p_geno <- PF.sc.loc_p_geno + geom_hline(
                                data = grand.means, aes(yintercept = pareto.front.scaling.location),
                                linetype = 2,
                                group = "cond")
PF.sc.loc_p_geno <- PF.sc.loc_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = ".all.", 
                                                              label = "p.signif", hide.ns = T)
PF.sc.loc_p_geno

```

Let's calculate the change in the pareto front scaling location relative to control conditions per accession as well.

```{r}
DAS4_avg <- summaryBy(data = DAS4, pareto.front.scaling.location ~ genotype + cond)
DAS4_avg
DAS_C <- subset(DAS4_avg, DAS4_avg$cond == "C")
DAS_C <- DAS_C[,c(1,3)]
colnames(DAS_C)[2] <- "PF.scaling.location.Control"
DAS_S <- subset(DAS4_avg, DAS4_avg$cond == "S")
DAS_S <- DAS_S[,c(1,3)]
colnames(DAS_S)[2] <- "PF.scaling.location.Salt"

DAS_STI <- merge(DAS_C, DAS_S, id="genotype")
DAS_STI$STI <- DAS_STI$PF.scaling.location.Salt / DAS_STI$PF.scaling.location.Control
DAS_STI
```

```{r}
PF.sc.loc_STI_histo <- gghistogram(DAS_STI, x = "STI",
                                    add = "mean", rug = TRUE) + xlab("Salt / Control")
PF.sc.loc_STI_histo

DAS4_avg

PF.sc.loc_histo <- gghistogram(DAS4_avg, x = "pareto.front.scaling.location.mean",
                                add = "mean", rug = TRUE,
                                color = "cond", fill = "cond",
                                palette = c("#00AFBB", "#E7B800")) + xlab("Pareto Front Scaling Location")
PF.sc.loc_histo

PF.sc.loc_histo <- gghistogram(all_MR5, x = "pareto.front.scaling.location",
                                add = "mean", rug = TRUE, facet.by = "DAS",
                                color = "cond", fill = "cond",
                                palette = c("#00AFBB", "#E7B800")) + xlab("Pareto Front Scaling Location")
PF.sc.loc_histo
```

